Overview

Dataset statistics

Number of variables30
Number of observations809711
Missing cells0
Missing cells (%)0.0%
Duplicate rows897
Duplicate rows (%)0.1%
Total size in memory185.3 MiB
Average record size in memory240.0 B

Variable types

Categorical22
Numeric8

Alerts

Dataset has 897 (0.1%) duplicate rowsDuplicates
COMISARIA has a high cardinality: 1104 distinct values High cardinality
DIRECCION has a high cardinality: 1712 distinct values High cardinality
DIST_CIA has a high cardinality: 816 distinct values High cardinality
DIST_HECHO has a high cardinality: 1428 distinct values High cardinality
LIBRO has a high cardinality: 55 distinct values High cardinality
PROV_CIA has a high cardinality: 188 distinct values High cardinality
PROV_HECHO has a high cardinality: 192 distinct values High cardinality
REGION has a high cardinality: 56 distinct values High cardinality
UBICACION has a high cardinality: 737837 distinct values High cardinality
PAIS_NATAL has a high cardinality: 211 distinct values High cardinality
FEC_REGISTRO_ANIO is highly correlated with FECHA_HORA_HECHO_ANIOHigh correlation
FEC_REGISTRO_MES is highly correlated with FECHA_HORA_HECHO_MESHigh correlation
FEC_REGISTRO_DIA is highly correlated with FECHA_HORA_HECHO_DIAHigh correlation
FECHA_HORA_HECHO_ANIO is highly correlated with FEC_REGISTRO_ANIOHigh correlation
FECHA_HORA_HECHO_MES is highly correlated with FEC_REGISTRO_MESHigh correlation
FECHA_HORA_HECHO_DIA is highly correlated with FEC_REGISTRO_DIAHigh correlation
FEC_REGISTRO_ANIO is highly correlated with FECHA_HORA_HECHO_ANIOHigh correlation
FEC_REGISTRO_MES is highly correlated with FECHA_HORA_HECHO_MESHigh correlation
FEC_REGISTRO_DIA is highly correlated with FECHA_HORA_HECHO_DIAHigh correlation
FECHA_HORA_HECHO_ANIO is highly correlated with FEC_REGISTRO_ANIOHigh correlation
FECHA_HORA_HECHO_MES is highly correlated with FEC_REGISTRO_MESHigh correlation
FECHA_HORA_HECHO_DIA is highly correlated with FEC_REGISTRO_DIAHigh correlation
FEC_REGISTRO_ANIO is highly correlated with FECHA_HORA_HECHO_ANIOHigh correlation
FEC_REGISTRO_MES is highly correlated with FECHA_HORA_HECHO_MESHigh correlation
FEC_REGISTRO_DIA is highly correlated with FECHA_HORA_HECHO_DIAHigh correlation
FECHA_HORA_HECHO_ANIO is highly correlated with FEC_REGISTRO_ANIOHigh correlation
FECHA_HORA_HECHO_MES is highly correlated with FEC_REGISTRO_MESHigh correlation
FECHA_HORA_HECHO_DIA is highly correlated with FEC_REGISTRO_DIAHigh correlation
REGION is highly correlated with TIPO and 2 other fieldsHigh correlation
MODALIDAD is highly correlated with SUB_TIPOHigh correlation
LIBRO is highly correlated with TIPO_DENUNCIAHigh correlation
TIPO is highly correlated with REGION and 1 other fieldsHigh correlation
SUB_TIPO is highly correlated with MODALIDADHigh correlation
SEXO is highly correlated with SIT_PERSONAHigh correlation
SIT_PERSONA is highly correlated with SEXOHigh correlation
TIPO_DENUNCIA is highly correlated with LIBROHigh correlation
FEC_REGISTRO_ANIO is highly correlated with TIPOHigh correlation
DPTO_HECHO is highly correlated with REGION and 1 other fieldsHigh correlation
DPTO_CIA is highly correlated with REGION and 1 other fieldsHigh correlation
DERIVADA_FISCALIA is highly correlated with EST_CIVIL and 2 other fieldsHigh correlation
DPTO_CIA is highly correlated with DPTO_HECHO and 3 other fieldsHigh correlation
DPTO_HECHO is highly correlated with DPTO_CIA and 3 other fieldsHigh correlation
EST_CIVIL is highly correlated with DERIVADA_FISCALIA and 2 other fieldsHigh correlation
LIBRO is highly correlated with DERIVADA_FISCALIA and 4 other fieldsHigh correlation
MODALIDAD is highly correlated with SUB_TIPOHigh correlation
REGION is highly correlated with DPTO_CIA and 6 other fieldsHigh correlation
SEXO is highly correlated with SIT_PERSONAHigh correlation
SIT_PERSONA is highly correlated with DERIVADA_FISCALIA and 3 other fieldsHigh correlation
SUB_TIPO is highly correlated with MODALIDADHigh correlation
TIPO is highly correlated with REGION and 1 other fieldsHigh correlation
TIPO_DENUNCIA is highly correlated with DPTO_CIA and 3 other fieldsHigh correlation
VIA is highly correlated with DPTO_CIA and 2 other fieldsHigh correlation
FEC_REGISTRO_ANIO is highly correlated with REGION and 2 other fieldsHigh correlation
FEC_REGISTRO_MES is highly correlated with FECHA_HORA_HECHO_MESHigh correlation
FEC_REGISTRO_DIA is highly correlated with FECHA_HORA_HECHO_DIAHigh correlation
FEC_REGISTRO_DIA_SEM is highly correlated with FECHA_HORA_HECHO_DIA_SEMHigh correlation
FECHA_HORA_HECHO_ANIO is highly correlated with FEC_REGISTRO_ANIOHigh correlation
FECHA_HORA_HECHO_MES is highly correlated with FEC_REGISTRO_MESHigh correlation
FECHA_HORA_HECHO_DIA is highly correlated with FEC_REGISTRO_DIAHigh correlation
FECHA_HORA_HECHO_DIA_SEM is highly correlated with FEC_REGISTRO_DIA_SEMHigh correlation
FEC_REGISTRO_DIA_SEM has 139264 (17.2%) zeros Zeros
FECHA_HORA_HECHO_DIA_SEM has 123397 (15.2%) zeros Zeros

Reproduction

Analysis started2022-08-07 18:12:54.994184
Analysis finished2022-08-07 18:14:35.781585
Duration1 minute and 40.79 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

COMISARIA
Categorical

HIGH CARDINALITY

Distinct1104
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
COMISARIA DE LA FAMILIA
 
51606
DE LA FAMILIA
 
12659
SOL DE ORO
 
10189
VITARTE
 
8917
BELLAVISTA
 
7764
Other values (1099)
718576 

Length

Max length46
Median length34
Mean length12.8305593
Min length3

Characters and Unicode

Total characters10389045
Distinct characters47
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63 ?
Unique (%)< 0.1%

Sample

1st rowHUANCHACO
2nd rowZAPALLAL
3rd rowZAPALLAL
4th rowCOMISARIA DE LA FAMILIA
5th rowZAPALLAL

Common Values

ValueCountFrequency (%)
COMISARIA DE LA FAMILIA51606
 
6.4%
DE LA FAMILIA12659
 
1.6%
SOL DE ORO10189
 
1.3%
VITARTE8917
 
1.1%
BELLAVISTA7764
 
1.0%
COMISARIA DE MUJERES DE VILLA EL SALVADOR7718
 
1.0%
SANTA ANITA7476
 
0.9%
EL TAMBO6538
 
0.8%
LA HUAYRONA5986
 
0.7%
HUAYCAN5932
 
0.7%
Other values (1094)684926
84.6%

Length

2022-08-07T13:14:35.876584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de189575
 
11.0%
la126647
 
7.4%
comisaria85010
 
4.9%
familia83299
 
4.8%
san51136
 
3.0%
el33864
 
2.0%
santa33087
 
1.9%
mujeres31703
 
1.8%
villa20728
 
1.2%
19965
 
1.2%
Other values (1129)1043525
60.7%

Most occurring characters

ValueCountFrequency (%)
A1860777
17.9%
910129
 
8.8%
I786831
 
7.6%
E726849
 
7.0%
L724819
 
7.0%
R609240
 
5.9%
O559398
 
5.4%
C529886
 
5.1%
S489108
 
4.7%
N473428
 
4.6%
Other values (37)2718580
26.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter9397089
90.5%
Space Separator910129
 
8.8%
Dash Punctuation46581
 
0.4%
Other Punctuation17720
 
0.2%
Decimal Number16082
 
0.2%
Other Letter654
 
< 0.1%
Open Punctuation395
 
< 0.1%
Close Punctuation395
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A1860777
19.8%
I786831
 
8.4%
E726849
 
7.7%
L724819
 
7.7%
R609240
 
6.5%
O559398
 
6.0%
C529886
 
5.6%
S489108
 
5.2%
N473428
 
5.0%
M444246
 
4.7%
Other values (22)2192507
23.3%
Decimal Number
ValueCountFrequency (%)
15340
33.2%
03706
23.0%
23038
18.9%
91940
 
12.1%
61366
 
8.5%
3654
 
4.1%
538
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
-32453
69.7%
14128
30.3%
Other Punctuation
ValueCountFrequency (%)
.17718
> 99.9%
/2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
910129
100.0%
Other Letter
ValueCountFrequency (%)
º654
100.0%
Open Punctuation
ValueCountFrequency (%)
(395
100.0%
Close Punctuation
ValueCountFrequency (%)
)395
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9397743
90.5%
Common991302
 
9.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A1860777
19.8%
I786831
 
8.4%
E726849
 
7.7%
L724819
 
7.7%
R609240
 
6.5%
O559398
 
6.0%
C529886
 
5.6%
S489108
 
5.2%
N473428
 
5.0%
M444246
 
4.7%
Other values (23)2193161
23.3%
Common
ValueCountFrequency (%)
910129
91.8%
-32453
 
3.3%
.17718
 
1.8%
14128
 
1.4%
15340
 
0.5%
03706
 
0.4%
23038
 
0.3%
91940
 
0.2%
61366
 
0.1%
3654
 
0.1%
Other values (4)830
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10312754
99.3%
None62163
 
0.6%
Punctuation14128
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A1860777
18.0%
910129
 
8.8%
I786831
 
7.6%
E726849
 
7.0%
L724819
 
7.0%
R609240
 
5.9%
O559398
 
5.4%
C529886
 
5.1%
S489108
 
4.7%
N473428
 
4.6%
Other values (29)2642289
25.6%
Punctuation
ValueCountFrequency (%)
14128
100.0%
None
ValueCountFrequency (%)
Ó12556
20.2%
Í12449
20.0%
Á11916
19.2%
É9728
15.6%
Ñ8047
12.9%
Ú6813
11.0%
º654
 
1.1%

DERIVADA_FISCALIA
Categorical

HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
JUZGADO DE FAMILIA
544548 
OTROS
193527 
FISCALÍA PENAL
 
24335
JUZGADO DE PAZ
 
21948
UNIDAD PNP
 
14614
Other values (5)
 
10739

Length

Max length26
Median length18
Mean length14.5090063
Min length2

Characters and Unicode

Total characters11748102
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJUZGADO DE FAMILIA
2nd rowUNIDAD PNP
3rd rowUNIDAD PNP
4th rowJUZGADO DE FAMILIA
5th rowUNIDAD PNP

Common Values

ValueCountFrequency (%)
JUZGADO DE FAMILIA544548
67.3%
OTROS193527
 
23.9%
FISCALÍA PENAL24335
 
3.0%
JUZGADO DE PAZ21948
 
2.7%
UNIDAD PNP14614
 
1.8%
FISCALÍA DE FAMILIA7382
 
0.9%
JUZGADO PENAL3301
 
0.4%
FISCALIA DE MEDIO AMBIENTE44
 
< 0.1%
FISCALIA DE MEDIO AM9
 
< 0.1%
RE3
 
< 0.1%

Length

2022-08-07T13:14:35.990585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-07T13:14:36.254614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
de573931
28.7%
juzgado569797
28.5%
familia551930
27.6%
otros193527
 
9.7%
fiscalía31717
 
1.6%
penal27636
 
1.4%
paz21948
 
1.1%
unidad14614
 
0.7%
pnp14614
 
0.7%
fiscalia53
 
< 0.1%
Other values (4)109
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A1801448
15.3%
1190165
10.1%
D1173009
10.0%
I1150394
9.8%
O956904
 
8.1%
L611336
 
5.2%
E601711
 
5.1%
Z591745
 
5.0%
U584411
 
5.0%
F583700
 
5.0%
Other values (11)2503279
21.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter10557937
89.9%
Space Separator1190165
 
10.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A1801448
17.1%
D1173009
11.1%
I1150394
10.9%
O956904
9.1%
L611336
 
5.8%
E601711
 
5.7%
Z591745
 
5.6%
U584411
 
5.5%
F583700
 
5.5%
J569797
 
5.4%
Other values (10)1933482
18.3%
Space Separator
ValueCountFrequency (%)
1190165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10557937
89.9%
Common1190165
 
10.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A1801448
17.1%
D1173009
11.1%
I1150394
10.9%
O956904
9.1%
L611336
 
5.8%
E601711
 
5.7%
Z591745
 
5.6%
U584411
 
5.5%
F583700
 
5.5%
J569797
 
5.4%
Other values (10)1933482
18.3%
Common
ValueCountFrequency (%)
1190165
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11716385
99.7%
None31717
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A1801448
15.4%
1190165
10.2%
D1173009
10.0%
I1150394
9.8%
O956904
 
8.2%
L611336
 
5.2%
E601711
 
5.1%
Z591745
 
5.1%
U584411
 
5.0%
F583700
 
5.0%
Other values (10)2471562
21.1%
None
ValueCountFrequency (%)
Í31717
100.0%

DIRECCION
Categorical

HIGH CARDINALITY

Distinct1712
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
AV : BUEN PASTOR
 
8663
Av. NICOLAS AYLLON S/N CARRETERA CENTRAL KM 7.5 VITARTE
 
8431
calle las gemas s/N la huayrona sjl
 
8210
AV. CESAR VALLEJO CUADRA 8
 
7718
CALLAO
 
7526
Other values (1707)
769163 

Length

Max length96
Median length58
Mean length25.32350308
Min length2

Characters and Unicode

Total characters20504719
Distinct characters92
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84 ?
Unique (%)< 0.1%

Sample

1st rowAV. LA RIVERA PLAZA SAN MARTIN
2nd rowJR GALILEA S/N
3rd rowJR GALILEA S/N
4th rowURB. CIUDAD DE DIOS MZA. Q LTE 1
5th rowJR GALILEA S/N

Common Values

ValueCountFrequency (%)
AV : BUEN PASTOR8663
 
1.1%
Av. NICOLAS AYLLON S/N CARRETERA CENTRAL KM 7.5 VITARTE8431
 
1.0%
calle las gemas s/N la huayrona sjl8210
 
1.0%
AV. CESAR VALLEJO CUADRA 87718
 
1.0%
CALLAO7526
 
0.9%
JR. SALVADOR6692
 
0.8%
JR LIBERTAD 12006597
 
0.8%
CALLE MONITOR HUASCAR6547
 
0.8%
AV MARISCAL CASTILLA CDRA 95869
 
0.7%
AV. LIBERTAD 384-ICA5623
 
0.7%
Other values (1702)737835
91.1%

Length

2022-08-07T13:14:36.401588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
av294429
 
7.9%
s/n219883
 
5.9%
jr172837
 
4.7%
calle125735
 
3.4%
de93567
 
2.5%
71726
 
1.9%
cdra44466
 
1.2%
san39260
 
1.1%
cuadra38110
 
1.0%
nro37049
 
1.0%
Other values (1964)2570361
69.3%

Most occurring characters

ValueCountFrequency (%)
2925277
 
14.3%
A2143912
 
10.5%
R1082684
 
5.3%
L871649
 
4.3%
E866390
 
4.2%
N839235
 
4.1%
C759538
 
3.7%
O743675
 
3.6%
S702378
 
3.4%
a645410
 
3.1%
Other values (82)8924571
43.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter12043534
58.7%
Lowercase Letter3352597
 
16.4%
Space Separator2925277
 
14.3%
Decimal Number1160251
 
5.7%
Other Punctuation815245
 
4.0%
Dash Punctuation152579
 
0.7%
Other Symbol39533
 
0.2%
Other Letter12638
 
0.1%
Open Punctuation1237
 
< 0.1%
Close Punctuation1237
 
< 0.1%
Other values (3)591
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A2143912
17.8%
R1082684
 
9.0%
L871649
 
7.2%
E866390
 
7.2%
N839235
 
7.0%
C759538
 
6.3%
O743675
 
6.2%
S702378
 
5.8%
I636105
 
5.3%
U454120
 
3.8%
Other values (21)2943848
24.4%
Lowercase Letter
ValueCountFrequency (%)
a645410
19.3%
l303759
9.1%
r278394
 
8.3%
e242895
 
7.2%
c222208
 
6.6%
n211188
 
6.3%
o193505
 
5.8%
i190077
 
5.7%
s189691
 
5.7%
m117125
 
3.5%
Other values (21)758345
22.6%
Decimal Number
ValueCountFrequency (%)
1260324
22.4%
0190290
16.4%
2149423
12.9%
3111077
9.6%
5103787
 
8.9%
889761
 
7.7%
487587
 
7.5%
759753
 
5.2%
954547
 
4.7%
653702
 
4.6%
Other Punctuation
ValueCountFrequency (%)
.552953
67.8%
/235852
28.9%
,10507
 
1.3%
:9861
 
1.2%
"3044
 
0.4%
#1846
 
0.2%
;1115
 
0.1%
'67
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
-151933
99.6%
646
 
0.4%
Other Letter
ValueCountFrequency (%)
º12409
98.2%
ª229
 
1.8%
Math Symbol
ValueCountFrequency (%)
|292
96.4%
+11
 
3.6%
Space Separator
ValueCountFrequency (%)
2925277
100.0%
Other Symbol
ValueCountFrequency (%)
°39533
100.0%
Open Punctuation
ValueCountFrequency (%)
(1237
100.0%
Close Punctuation
ValueCountFrequency (%)
)1237
100.0%
Initial Punctuation
ValueCountFrequency (%)
144
100.0%
Final Punctuation
ValueCountFrequency (%)
144
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin15408769
75.1%
Common5095950
 
24.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A2143912
 
13.9%
R1082684
 
7.0%
L871649
 
5.7%
E866390
 
5.6%
N839235
 
5.4%
C759538
 
4.9%
O743675
 
4.8%
S702378
 
4.6%
a645410
 
4.2%
I636105
 
4.1%
Other values (54)6117793
39.7%
Common
ValueCountFrequency (%)
2925277
57.4%
.552953
 
10.9%
1260324
 
5.1%
/235852
 
4.6%
0190290
 
3.7%
-151933
 
3.0%
2149423
 
2.9%
3111077
 
2.2%
5103787
 
2.0%
889761
 
1.8%
Other values (18)325273
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII20424636
99.6%
None79149
 
0.4%
Punctuation934
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2925277
 
14.3%
A2143912
 
10.5%
R1082684
 
5.3%
L871649
 
4.3%
E866390
 
4.2%
N839235
 
4.1%
C759538
 
3.7%
O743675
 
3.6%
S702378
 
3.4%
a645410
 
3.2%
Other values (66)8844488
43.3%
None
ValueCountFrequency (%)
°39533
49.9%
Ñ18602
23.5%
º12409
 
15.7%
ñ3749
 
4.7%
ó2919
 
3.7%
é534
 
0.7%
í466
 
0.6%
Ó307
 
0.4%
ª229
 
0.3%
Á147
 
0.2%
Other values (3)254
 
0.3%
Punctuation
ValueCountFrequency (%)
646
69.2%
144
 
15.4%
144
 
15.4%

DIST_CIA
Categorical

HIGH CARDINALITY

Distinct816
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
SAN JUAN DE LURIGANCHO
 
31280
LIMA
 
21554
ATE
 
20693
CALLAO
 
20420
LOS OLIVOS
 
18345
Other values (811)
697419 

Length

Max length36
Median length25
Mean length10.08207496
Min length3

Characters and Unicode

Total characters8163567
Distinct characters33
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)< 0.1%

Sample

1st rowHUANCHACO
2nd rowPUENTE PIEDRA
3rd rowPUENTE PIEDRA
4th rowNUEVO CHIMBOTE
5th rowPUENTE PIEDRA

Common Values

ValueCountFrequency (%)
SAN JUAN DE LURIGANCHO31280
 
3.9%
LIMA21554
 
2.7%
ATE20693
 
2.6%
CALLAO20420
 
2.5%
LOS OLIVOS18345
 
2.3%
CHICLAYO15714
 
1.9%
COMAS14893
 
1.8%
PIURA12829
 
1.6%
VILLA EL SALVADOR11836
 
1.5%
SAN MARTIN DE PORRES11824
 
1.5%
Other values (806)630323
77.8%

Length

2022-08-07T13:14:36.526585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
san83405
 
6.3%
de77217
 
5.8%
juan42784
 
3.2%
lurigancho37983
 
2.9%
el31350
 
2.4%
la25328
 
1.9%
villa23993
 
1.8%
lima21554
 
1.6%
ate20693
 
1.6%
callao20420
 
1.5%
Other values (853)936989
70.9%

Most occurring characters

ValueCountFrequency (%)
A1383242
16.9%
O626523
 
7.7%
L596621
 
7.3%
I524139
 
6.4%
512005
 
6.3%
C509399
 
6.2%
N504227
 
6.2%
R503402
 
6.2%
E490184
 
6.0%
S398280
 
4.9%
Other values (23)2115545
25.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter7634026
93.5%
Space Separator512005
 
6.3%
Dash Punctuation9111
 
0.1%
Decimal Number8170
 
0.1%
Connector Punctuation195
 
< 0.1%
Other Punctuation60
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A1383242
18.1%
O626523
 
8.2%
L596621
 
7.8%
I524139
 
6.9%
C509399
 
6.7%
N504227
 
6.6%
R503402
 
6.6%
E490184
 
6.4%
S398280
 
5.2%
U378528
 
5.0%
Other values (17)1719481
22.5%
Decimal Number
ValueCountFrequency (%)
24085
50.0%
64085
50.0%
Space Separator
ValueCountFrequency (%)
512005
100.0%
Dash Punctuation
ValueCountFrequency (%)
-9111
100.0%
Connector Punctuation
ValueCountFrequency (%)
_195
100.0%
Other Punctuation
ValueCountFrequency (%)
.60
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7634026
93.5%
Common529541
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A1383242
18.1%
O626523
 
8.2%
L596621
 
7.8%
I524139
 
6.9%
C509399
 
6.7%
N504227
 
6.6%
R503402
 
6.6%
E490184
 
6.4%
S398280
 
5.2%
U378528
 
5.0%
Other values (17)1719481
22.5%
Common
ValueCountFrequency (%)
512005
96.7%
-9111
 
1.7%
24085
 
0.8%
64085
 
0.8%
_195
 
< 0.1%
.60
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8150335
99.8%
None13232
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A1383242
17.0%
O626523
 
7.7%
L596621
 
7.3%
I524139
 
6.4%
512005
 
6.3%
C509399
 
6.3%
N504227
 
6.2%
R503402
 
6.2%
E490184
 
6.0%
S398280
 
4.9%
Other values (22)2102313
25.8%
None
ValueCountFrequency (%)
Ñ13232
100.0%

DIST_HECHO
Categorical

HIGH CARDINALITY

Distinct1428
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
SAN JUAN DE LURIGANCHO
 
30261
ATE
 
20376
CALLAO
 
19337
SAN MARTIN DE PORRES
 
17765
LIMA
 
17340
Other values (1423)
704632 

Length

Max length36
Median length27
Mean length10.38307742
Min length3

Characters and Unicode

Total characters8407292
Distinct characters41
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique137 ?
Unique (%)< 0.1%

Sample

1st rowHUANCHACO
2nd rowPUENTE PIEDRA
3rd rowPUENTE PIEDRA
4th rowNUEVO CHIMBOTE
5th rowPUENTE PIEDRA

Common Values

ValueCountFrequency (%)
SAN JUAN DE LURIGANCHO30261
 
3.7%
ATE20376
 
2.5%
CALLAO19337
 
2.4%
SAN MARTIN DE PORRES17765
 
2.2%
LIMA17340
 
2.1%
COMAS16191
 
2.0%
VILLA EL SALVADOR13928
 
1.7%
LOS OLIVOS13356
 
1.6%
VILLA MARIA DEL TRIUNFO13174
 
1.6%
PIURA13061
 
1.6%
Other values (1418)634922
78.4%

Length

2022-08-07T13:14:36.642586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
san90451
 
6.6%
de82666
 
6.1%
juan44907
 
3.3%
lurigancho37789
 
2.8%
el33669
 
2.5%
la28694
 
2.1%
villa27552
 
2.0%
ate20376
 
1.5%
callao19337
 
1.4%
miraflores18288
 
1.3%
Other values (1450)960494
70.4%

Most occurring characters

ValueCountFrequency (%)
A1418266
16.9%
O621212
 
7.4%
L598650
 
7.1%
554512
 
6.6%
R540166
 
6.4%
N534215
 
6.4%
I533291
 
6.3%
E523516
 
6.2%
C502261
 
6.0%
S411637
 
4.9%
Other values (31)2169566
25.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter7834863
93.2%
Space Separator554512
 
6.6%
Dash Punctuation9936
 
0.1%
Decimal Number7624
 
0.1%
Connector Punctuation191
 
< 0.1%
Lowercase Letter96
 
< 0.1%
Other Punctuation70
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A1418266
18.1%
O621212
 
7.9%
L598650
 
7.6%
R540166
 
6.9%
N534215
 
6.8%
I533291
 
6.8%
E523516
 
6.7%
C502261
 
6.4%
S411637
 
5.3%
U382960
 
4.9%
Other values (17)1768689
22.6%
Lowercase Letter
ValueCountFrequency (%)
e24
25.0%
c16
16.7%
o16
16.7%
l8
 
8.3%
i8
 
8.3%
n8
 
8.3%
a8
 
8.3%
t8
 
8.3%
Decimal Number
ValueCountFrequency (%)
23812
50.0%
63812
50.0%
Space Separator
ValueCountFrequency (%)
554512
100.0%
Dash Punctuation
ValueCountFrequency (%)
-9936
100.0%
Connector Punctuation
ValueCountFrequency (%)
_191
100.0%
Other Punctuation
ValueCountFrequency (%)
.70
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7834959
93.2%
Common572333
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A1418266
18.1%
O621212
 
7.9%
L598650
 
7.6%
R540166
 
6.9%
N534215
 
6.8%
I533291
 
6.8%
E523516
 
6.7%
C502261
 
6.4%
S411637
 
5.3%
U382960
 
4.9%
Other values (25)1768785
22.6%
Common
ValueCountFrequency (%)
554512
96.9%
-9936
 
1.7%
23812
 
0.7%
63812
 
0.7%
_191
 
< 0.1%
.70
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8394945
99.9%
None12347
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A1418266
16.9%
O621212
 
7.4%
L598650
 
7.1%
554512
 
6.6%
R540166
 
6.4%
N534215
 
6.4%
I533291
 
6.4%
E523516
 
6.2%
C502261
 
6.0%
S411637
 
4.9%
Other values (30)2157219
25.7%
None
ValueCountFrequency (%)
Ñ12347
100.0%

DPTO_CIA
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
LIMA
302953 
AREQUIPA
76022 
PIURA
43124 
CUSCO
38287 
LA LIBERTAD
36855 
Other values (21)
312470 

Length

Max length13
Median length12
Mean length5.823868022
Min length3

Characters and Unicode

Total characters4715650
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLA LIBERTAD
2nd rowLIMA
3rd rowLIMA
4th rowANCASH
5th rowLIMA

Common Values

ValueCountFrequency (%)
LIMA302953
37.4%
AREQUIPA76022
 
9.4%
PIURA43124
 
5.3%
CUSCO38287
 
4.7%
LA LIBERTAD36855
 
4.6%
LAMBAYEQUE36477
 
4.5%
CALLAO36002
 
4.4%
JUNIN34246
 
4.2%
ICA31257
 
3.9%
ANCASH28736
 
3.5%
Other values (16)145752
18.0%

Length

2022-08-07T13:14:36.743614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lima302953
35.0%
arequipa76022
 
8.8%
piura43124
 
5.0%
cusco38287
 
4.4%
la36855
 
4.3%
libertad36855
 
4.3%
lambayeque36477
 
4.2%
callao36002
 
4.2%
junin34246
 
4.0%
ica31257
 
3.6%
Other values (20)192417
22.3%

Most occurring characters

ValueCountFrequency (%)
A1051464
22.3%
I560687
11.9%
L505605
10.7%
M408910
 
8.7%
U324978
 
6.9%
C287513
 
6.1%
E223802
 
4.7%
R211005
 
4.5%
N168285
 
3.6%
O156165
 
3.3%
Other values (13)817236
17.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4660866
98.8%
Space Separator54784
 
1.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A1051464
22.6%
I560687
12.0%
L505605
10.8%
M408910
 
8.8%
U324978
 
7.0%
C287513
 
6.2%
E223802
 
4.8%
R211005
 
4.5%
N168285
 
3.6%
O156165
 
3.4%
Other values (12)762452
16.4%
Space Separator
ValueCountFrequency (%)
54784
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4660866
98.8%
Common54784
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A1051464
22.6%
I560687
12.0%
L505605
10.8%
M408910
 
8.8%
U324978
 
7.0%
C287513
 
6.2%
E223802
 
4.8%
R211005
 
4.5%
N168285
 
3.6%
O156165
 
3.4%
Other values (12)762452
16.4%
Common
ValueCountFrequency (%)
54784
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4715650
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A1051464
22.3%
I560687
11.9%
L505605
10.7%
M408910
 
8.7%
U324978
 
6.9%
C287513
 
6.1%
E223802
 
4.7%
R211005
 
4.5%
N168285
 
3.6%
O156165
 
3.3%
Other values (13)817236
17.3%

DPTO_HECHO
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
LIMA
301847 
AREQUIPA
76280 
PIURA
43288 
CUSCO
38251 
LA LIBERTAD
36851 
Other values (22)
313194 

Length

Max length13
Median length12
Mean length5.827952195
Min length3

Characters and Unicode

Total characters4718957
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLA LIBERTAD
2nd rowLIMA
3rd rowLIMA
4th rowANCASH
5th rowLIMA

Common Values

ValueCountFrequency (%)
LIMA301847
37.3%
AREQUIPA76280
 
9.4%
PIURA43288
 
5.3%
CUSCO38251
 
4.7%
LA LIBERTAD36851
 
4.6%
LAMBAYEQUE36832
 
4.5%
CALLAO36011
 
4.4%
JUNIN34426
 
4.3%
ICA31302
 
3.9%
ANCASH29024
 
3.6%
Other values (17)145599
18.0%

Length

2022-08-07T13:14:36.835614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lima301847
34.9%
arequipa76280
 
8.8%
piura43288
 
5.0%
cusco38251
 
4.4%
la36851
 
4.3%
libertad36851
 
4.3%
lambayeque36832
 
4.3%
callao36011
 
4.2%
junin34426
 
4.0%
ica31302
 
3.6%
Other values (22)192651
22.3%

Most occurring characters

ValueCountFrequency (%)
A1051886
22.3%
I560189
11.9%
L504807
10.7%
M407837
 
8.6%
U325821
 
6.9%
C287788
 
6.1%
E224659
 
4.8%
R211168
 
4.5%
N169081
 
3.6%
O155990
 
3.3%
Other values (13)819731
17.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4664078
98.8%
Space Separator54879
 
1.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A1051886
22.6%
I560189
12.0%
L504807
10.8%
M407837
 
8.7%
U325821
 
7.0%
C287788
 
6.2%
E224659
 
4.8%
R211168
 
4.5%
N169081
 
3.6%
O155990
 
3.3%
Other values (12)764852
16.4%
Space Separator
ValueCountFrequency (%)
54879
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4664078
98.8%
Common54879
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A1051886
22.6%
I560189
12.0%
L504807
10.8%
M407837
 
8.7%
U325821
 
7.0%
C287788
 
6.2%
E224659
 
4.8%
R211168
 
4.5%
N169081
 
3.6%
O155990
 
3.3%
Other values (12)764852
16.4%
Common
ValueCountFrequency (%)
54879
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4718957
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A1051886
22.3%
I560189
11.9%
L504807
10.7%
M407837
 
8.6%
U325821
 
6.9%
C287788
 
6.1%
E224659
 
4.8%
R211168
 
4.5%
N169081
 
3.6%
O155990
 
3.3%
Other values (13)819731
17.4%

EDAD
Real number (ℝ≥0)

Distinct58
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.9457646
Minimum18
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2022-08-07T13:14:36.934616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q127
median32
Q341
95-th percentile57
Maximum75
Range57
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.42874197
Coefficient of variation (CV)0.3270422641
Kurtosis0.5254670608
Mean34.9457646
Median Absolute Deviation (MAD)7
Skewness0.8824736196
Sum28295970
Variance130.6161431
MonotonicityNot monotonic
2022-08-07T13:14:37.038585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3188579
 
10.9%
3025454
 
3.1%
2725416
 
3.1%
2825339
 
3.1%
2925250
 
3.1%
3224964
 
3.1%
2624955
 
3.1%
3324362
 
3.0%
2424248
 
3.0%
2524202
 
3.0%
Other values (48)496942
61.4%
ValueCountFrequency (%)
1817183
2.1%
1918809
2.3%
2020708
2.6%
2121788
2.7%
2223097
2.9%
2323857
2.9%
2424248
3.0%
2524202
3.0%
2624955
3.1%
2725416
3.1%
ValueCountFrequency (%)
75861
0.1%
741071
0.1%
731103
0.1%
721216
0.2%
711361
0.2%
701396
0.2%
691586
0.2%
681707
0.2%
671792
0.2%
662022
0.2%

EST_CIVIL
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
SOLTERO(A)
571263 
CASADO(A)
136351 
CONVIVIENTE
72767 
NO INDICA
 
17355
DIVORCIADO(A)
 
7161
Other values (2)
 
4814

Length

Max length13
Median length10
Mean length9.91466585
Min length4

Characters and Unicode

Total characters8028014
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCONVIVIENTE
2nd rowSOLTERO(A)
3rd rowSOLTERO(A)
4th rowSOLTERO(A)
5th rowSOLTERO(A)

Common Values

ValueCountFrequency (%)
SOLTERO(A)571263
70.6%
CASADO(A)136351
 
16.8%
CONVIVIENTE72767
 
9.0%
NO INDICA17355
 
2.1%
DIVORCIADO(A)7161
 
0.9%
VIUDO(A)4811
 
0.6%
ECSL3
 
< 0.1%

Length

2022-08-07T13:14:37.138613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-07T13:14:37.242585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
soltero(a571263
69.1%
casado(a136351
 
16.5%
conviviente72767
 
8.8%
no17355
 
2.1%
indica17355
 
2.1%
divorciado(a7161
 
0.9%
viudo(a4811
 
0.6%
ecsl3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
O1388132
17.3%
A1016804
12.7%
(719586
9.0%
)719586
9.0%
E716800
8.9%
S707617
8.8%
T644030
8.0%
R578424
7.2%
L571266
7.1%
C233637
 
2.9%
Other values (6)732132
9.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter6571487
81.9%
Open Punctuation719586
 
9.0%
Close Punctuation719586
 
9.0%
Space Separator17355
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O1388132
21.1%
A1016804
15.5%
E716800
10.9%
S707617
10.8%
T644030
9.8%
R578424
8.8%
L571266
8.7%
C233637
 
3.6%
I199377
 
3.0%
N180244
 
2.7%
Other values (3)335156
 
5.1%
Open Punctuation
ValueCountFrequency (%)
(719586
100.0%
Close Punctuation
ValueCountFrequency (%)
)719586
100.0%
Space Separator
ValueCountFrequency (%)
17355
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6571487
81.9%
Common1456527
 
18.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
O1388132
21.1%
A1016804
15.5%
E716800
10.9%
S707617
10.8%
T644030
9.8%
R578424
8.8%
L571266
8.7%
C233637
 
3.6%
I199377
 
3.0%
N180244
 
2.7%
Other values (3)335156
 
5.1%
Common
ValueCountFrequency (%)
(719586
49.4%
)719586
49.4%
17355
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII8028014
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O1388132
17.3%
A1016804
12.7%
(719586
9.0%
)719586
9.0%
E716800
8.9%
S707617
8.8%
T644030
8.0%
R578424
7.2%
L571266
7.1%
C233637
 
2.9%
Other values (6)732132
9.1%

LIBRO
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct55
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
[FAM] DENUNCIA VIOLENCIA FAMILIAR
440843 
[FAM] ACTA DE DENUNCIA VERBAL
183958 
[FAM] ACTA DE INTERVENCION
57805 
[FAM] DENUNCIA ABANDONO Y RETIRO DE HOGAR
 
33195
[FAM] OCURRENCIA VIOLENCIA FAMILIAR
 
31752
Other values (50)
62158 

Length

Max length72
Median length33
Mean length31.96004871
Min length2

Characters and Unicode

Total characters25878403
Distinct characters38
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st row[DEINPOL] ACTA DE DENUNCIA VERBAL
2nd row[FAM] DENUNCIA VIOLENCIA FAMILIAR
3rd row[FAM] DENUNCIA VIOLENCIA FAMILIAR
4th row[FAM] ACTA DE DENUNCIA VERBAL
5th row[FAM] DENUNCIA VIOLENCIA FAMILIAR

Common Values

ValueCountFrequency (%)
[FAM] DENUNCIA VIOLENCIA FAMILIAR440843
54.4%
[FAM] ACTA DE DENUNCIA VERBAL183958
22.7%
[FAM] ACTA DE INTERVENCION57805
 
7.1%
[FAM] DENUNCIA ABANDONO Y RETIRO DE HOGAR33195
 
4.1%
[FAM] OCURRENCIA VIOLENCIA FAMILIAR31752
 
3.9%
[DEINPOL] DENUNCIA DIRECTA DELITO24609
 
3.0%
[DEINPOL] ACTA DE DENUNCIA VERBAL12030
 
1.5%
[FAM] OCURRENCIA MENORES6859
 
0.8%
[DEINPOL] ACTA DE INTERVENCION4025
 
0.5%
[FAM] DENUNCIA LIBRO DE MENORES3911
 
0.5%
Other values (45)10724
 
1.3%

Length

2022-08-07T13:14:37.361586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fam765412
21.6%
denuncia703278
19.8%
violencia475325
13.4%
familiar475325
13.4%
de302159
 
8.5%
acta259654
 
7.3%
verbal197097
 
5.6%
intervencion62558
 
1.8%
ocurrencia43868
 
1.2%
deinpol43059
 
1.2%
Other values (55)223448
 
6.3%

Most occurring characters

ValueCountFrequency (%)
A3795077
14.7%
I2905386
11.2%
2741727
10.6%
N2239765
 
8.7%
E2009185
 
7.8%
C1624833
 
6.3%
M1256866
 
4.9%
F1241186
 
4.8%
L1230657
 
4.8%
D1136418
 
4.4%
Other values (28)5697303
22.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter21508850
83.1%
Space Separator2741727
 
10.6%
Open Punctuation809714
 
3.1%
Close Punctuation809714
 
3.1%
Dash Punctuation7867
 
< 0.1%
Other Punctuation338
 
< 0.1%
Decimal Number193
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A3795077
17.6%
I2905386
13.5%
N2239765
10.4%
E2009185
9.3%
C1624833
7.6%
M1256866
 
5.8%
F1241186
 
5.8%
L1230657
 
5.7%
D1136418
 
5.3%
R971644
 
4.5%
Other values (13)3097833
14.4%
Decimal Number
ValueCountFrequency (%)
0187
96.9%
12
 
1.0%
91
 
0.5%
31
 
0.5%
51
 
0.5%
71
 
0.5%
Other Punctuation
ValueCountFrequency (%)
/187
55.3%
,149
44.1%
.2
 
0.6%
Open Punctuation
ValueCountFrequency (%)
[809708
> 99.9%
(6
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
]809708
> 99.9%
)6
 
< 0.1%
Space Separator
ValueCountFrequency (%)
2741727
100.0%
Dash Punctuation
ValueCountFrequency (%)
-7867
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin21508850
83.1%
Common4369553
 
16.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A3795077
17.6%
I2905386
13.5%
N2239765
10.4%
E2009185
9.3%
C1624833
7.6%
M1256866
 
5.8%
F1241186
 
5.8%
L1230657
 
5.7%
D1136418
 
5.3%
R971644
 
4.5%
Other values (13)3097833
14.4%
Common
ValueCountFrequency (%)
2741727
62.7%
[809708
 
18.5%
]809708
 
18.5%
-7867
 
0.2%
/187
 
< 0.1%
0187
 
< 0.1%
,149
 
< 0.1%
(6
 
< 0.1%
)6
 
< 0.1%
.2
 
< 0.1%
Other values (5)6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII25878403
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A3795077
14.7%
I2905386
11.2%
2741727
10.6%
N2239765
 
8.7%
E2009185
 
7.8%
C1624833
 
6.3%
M1256866
 
4.9%
F1241186
 
4.8%
L1230657
 
4.8%
D1136418
 
4.4%
Other values (28)5697303
22.0%

MODALIDAD
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
VIOLENCIA PSICOLOGICA
337059 
VIOLENCIA FISICA Y PSICOLOGICA
319312 
VIOLENCIA FISICA
137688 
VIOLENCIA ECONOMICA O PATRIMONIAL
 
11819
MALTRATO SIN LESION
 
3435
Other values (2)
 
398

Length

Max length33
Median length30
Mean length23.86185318
Min length13

Characters and Unicode

Total characters19321205
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMALTRATO SIN LESION
2nd rowMALTRATO SIN LESION
3rd rowMALTRATO SIN LESION
4th rowVIOLENCIA FISICA
5th rowMALTRATO SIN LESION

Common Values

ValueCountFrequency (%)
VIOLENCIA PSICOLOGICA337059
41.6%
VIOLENCIA FISICA Y PSICOLOGICA319312
39.4%
VIOLENCIA FISICA137688
17.0%
VIOLENCIA ECONOMICA O PATRIMONIAL11819
 
1.5%
MALTRATO SIN LESION3435
 
0.4%
AMENAZA GRAVE266
 
< 0.1%
COACCION GRAVE132
 
< 0.1%

Length

2022-08-07T13:14:37.469614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-07T13:14:37.587612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
violencia805878
35.3%
psicologica656371
28.7%
fisica457000
20.0%
y319312
 
14.0%
economica11819
 
0.5%
o11819
 
0.5%
patrimonial11819
 
0.5%
maltrato3435
 
0.2%
sin3435
 
0.2%
lesion3435
 
0.2%
Other values (3)796
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
I3880957
20.1%
C2599654
13.5%
O2173030
11.2%
A1962904
10.2%
L1480938
 
7.7%
1475408
 
7.6%
S1120241
 
5.8%
N836784
 
4.3%
E821796
 
4.3%
V806276
 
4.2%
Other values (8)2163217
11.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter17845797
92.4%
Space Separator1475408
 
7.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I3880957
21.7%
C2599654
14.6%
O2173030
12.2%
A1962904
11.0%
L1480938
 
8.3%
S1120241
 
6.3%
N836784
 
4.7%
E821796
 
4.6%
V806276
 
4.5%
P668190
 
3.7%
Other values (7)1495027
 
8.4%
Space Separator
ValueCountFrequency (%)
1475408
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin17845797
92.4%
Common1475408
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
I3880957
21.7%
C2599654
14.6%
O2173030
12.2%
A1962904
11.0%
L1480938
 
8.3%
S1120241
 
6.3%
N836784
 
4.7%
E821796
 
4.6%
V806276
 
4.5%
P668190
 
3.7%
Other values (7)1495027
 
8.4%
Common
ValueCountFrequency (%)
1475408
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII19321205
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I3880957
20.1%
C2599654
13.5%
O2173030
11.2%
A1962904
10.2%
L1480938
 
7.7%
1475408
 
7.6%
S1120241
 
5.8%
N836784
 
4.3%
E821796
 
4.3%
V806276
 
4.2%
Other values (8)2163217
11.2%

PROV_CIA
Categorical

HIGH CARDINALITY

Distinct188
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
LIMA
280012 
AREQUIPA
63260 
CALLAO
 
36002
CHICLAYO
 
28972
TRUJILLO
 
26810
Other values (183)
374655 

Length

Max length25
Median length23
Mean length6.130660199
Min length3

Characters and Unicode

Total characters4964063
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowTRUJILLO
2nd rowLIMA
3rd rowLIMA
4th rowSANTA
5th rowLIMA

Common Values

ValueCountFrequency (%)
LIMA280012
34.6%
AREQUIPA63260
 
7.8%
CALLAO36002
 
4.4%
CHICLAYO28972
 
3.6%
TRUJILLO26810
 
3.3%
PIURA24363
 
3.0%
CUSCO22745
 
2.8%
HUANCAYO20063
 
2.5%
TACNA14867
 
1.8%
ICA14822
 
1.8%
Other values (178)277795
34.3%

Length

2022-08-07T13:14:37.709611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lima280012
32.9%
arequipa63260
 
7.4%
callao36002
 
4.2%
chiclayo28972
 
3.4%
trujillo26810
 
3.2%
piura24363
 
2.9%
cusco22745
 
2.7%
huancayo20063
 
2.4%
tacna14867
 
1.7%
ica14822
 
1.7%
Other values (204)318609
37.5%

Most occurring characters

ValueCountFrequency (%)
A1106945
22.3%
L544059
11.0%
I526149
10.6%
M377978
 
7.6%
C369058
 
7.4%
U278579
 
5.6%
O277904
 
5.6%
R224509
 
4.5%
N203306
 
4.1%
E151596
 
3.1%
Other values (17)903980
18.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4923249
99.2%
Space Separator40814
 
0.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A1106945
22.5%
L544059
11.1%
I526149
10.7%
M377978
 
7.7%
C369058
 
7.5%
U278579
 
5.7%
O277904
 
5.6%
R224509
 
4.6%
N203306
 
4.1%
E151596
 
3.1%
Other values (16)863166
17.5%
Space Separator
ValueCountFrequency (%)
40814
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4923249
99.2%
Common40814
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A1106945
22.5%
L544059
11.1%
I526149
10.7%
M377978
 
7.7%
C369058
 
7.5%
U278579
 
5.7%
O277904
 
5.6%
R224509
 
4.6%
N203306
 
4.1%
E151596
 
3.1%
Other values (16)863166
17.5%
Common
ValueCountFrequency (%)
40814
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4954502
99.8%
None9561
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A1106945
22.3%
L544059
11.0%
I526149
10.6%
M377978
 
7.6%
C369058
 
7.4%
U278579
 
5.6%
O277904
 
5.6%
R224509
 
4.5%
N203306
 
4.1%
E151596
 
3.1%
Other values (16)894419
18.1%
None
ValueCountFrequency (%)
Ñ9561
100.0%

PROV_HECHO
Categorical

HIGH CARDINALITY

Distinct192
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
LIMA
278765 
AREQUIPA
63738 
CALLAO
 
36011
CHICLAYO
 
29205
TRUJILLO
 
28224
Other values (187)
373768 

Length

Max length25
Median length23
Mean length6.138041845
Min length3

Characters and Unicode

Total characters4970040
Distinct characters35
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowTRUJILLO
2nd rowLIMA
3rd rowLIMA
4th rowSANTA
5th rowLIMA

Common Values

ValueCountFrequency (%)
LIMA278765
34.4%
AREQUIPA63738
 
7.9%
CALLAO36011
 
4.4%
CHICLAYO29205
 
3.6%
TRUJILLO28224
 
3.5%
PIURA24452
 
3.0%
CUSCO22465
 
2.8%
HUANCAYO20889
 
2.6%
TACNA14872
 
1.8%
ICA14751
 
1.8%
Other values (182)276339
34.1%

Length

2022-08-07T13:14:37.811585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lima278765
32.8%
arequipa63738
 
7.5%
callao36011
 
4.2%
chiclayo29205
 
3.4%
trujillo28224
 
3.3%
piura24452
 
2.9%
cusco22465
 
2.6%
huancayo20889
 
2.5%
tacna14872
 
1.7%
ica14751
 
1.7%
Other values (210)316945
37.3%

Most occurring characters

ValueCountFrequency (%)
A1106346
22.3%
L549314
11.1%
I527233
10.6%
M375164
 
7.5%
C366253
 
7.4%
U280795
 
5.6%
O277975
 
5.6%
R227205
 
4.6%
N203439
 
4.1%
E151407
 
3.0%
Other values (25)904909
18.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4929338
99.2%
Space Separator40606
 
0.8%
Lowercase Letter96
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A1106346
22.4%
L549314
11.1%
I527233
10.7%
M375164
 
7.6%
C366253
 
7.4%
U280795
 
5.7%
O277975
 
5.6%
R227205
 
4.6%
N203439
 
4.1%
E151407
 
3.1%
Other values (16)864207
17.5%
Lowercase Letter
ValueCountFrequency (%)
e24
25.0%
c16
16.7%
o16
16.7%
l8
 
8.3%
i8
 
8.3%
n8
 
8.3%
a8
 
8.3%
t8
 
8.3%
Space Separator
ValueCountFrequency (%)
40606
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4929434
99.2%
Common40606
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A1106346
22.4%
L549314
11.1%
I527233
10.7%
M375164
 
7.6%
C366253
 
7.4%
U280795
 
5.7%
O277975
 
5.6%
R227205
 
4.6%
N203439
 
4.1%
E151407
 
3.1%
Other values (24)864303
17.5%
Common
ValueCountFrequency (%)
40606
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4960503
99.8%
None9537
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A1106346
22.3%
L549314
11.1%
I527233
10.6%
M375164
 
7.6%
C366253
 
7.4%
U280795
 
5.7%
O277975
 
5.6%
R227205
 
4.6%
N203439
 
4.1%
E151407
 
3.1%
Other values (24)895372
18.1%
None
ValueCountFrequency (%)
Ñ9537
100.0%

REGION
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
REGPOL - LIMA
226344 
REGPOL - LIMA
67953 
REGPOL - AREQUIPA
59797 
REGPOL - PIURA
 
30717
REGPOL - CALLAO
 
27896
Other values (51)
397004 

Length

Max length32
Median length23
Mean length16.45044713
Min length6

Characters and Unicode

Total characters13320108
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowREGPOL - LA LIBERTAD
2nd rowREGPOL - LIMA
3rd rowREGPOL - LIMA
4th rowREGPOL - HUARAZ
5th rowREGPOL - LIMA

Common Values

ValueCountFrequency (%)
REGPOL - LIMA226344
28.0%
REGPOL - LIMA67953
 
8.4%
REGPOL - AREQUIPA59797
 
7.4%
REGPOL - PIURA30717
 
3.8%
REGPOL - CALLAO27896
 
3.4%
REGPOL - LAMBAYEQUE27601
 
3.4%
REGPOL - LA LIBERTAD27144
 
3.4%
REGPOL - CUSCO24532
 
3.0%
REGPOL - ICA23620
 
2.9%
REGPOL - HUANCAYO22881
 
2.8%
Other values (46)271226
33.5%

Length

2022-08-07T13:14:37.913585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
791240
31.8%
regpol773753
31.1%
lima294297
 
11.8%
arequipa76332
 
3.1%
piura43310
 
1.7%
la36825
 
1.5%
libertad36825
 
1.5%
lambayeque36765
 
1.5%
callao36266
 
1.5%
huancayo32967
 
1.3%
Other values (33)327408
13.2%

Most occurring characters

ValueCountFrequency (%)
2796655
21.0%
L1278291
9.6%
A1151829
8.6%
R1029204
 
7.7%
E1019529
 
7.7%
P970755
 
7.3%
O958783
 
7.2%
-791240
 
5.9%
G789164
 
5.9%
I588479
 
4.4%
Other values (15)1946179
14.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter9732213
73.1%
Space Separator2796655
 
21.0%
Dash Punctuation791240
 
5.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L1278291
13.1%
A1151829
11.8%
R1029204
10.6%
E1019529
10.5%
P970755
10.0%
O958783
9.9%
G789164
8.1%
I588479
6.0%
M406063
 
4.2%
U368580
 
3.8%
Other values (13)1171536
12.0%
Space Separator
ValueCountFrequency (%)
2796655
100.0%
Dash Punctuation
ValueCountFrequency (%)
-791240
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9732213
73.1%
Common3587895
 
26.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
L1278291
13.1%
A1151829
11.8%
R1029204
10.6%
E1019529
10.5%
P970755
10.0%
O958783
9.9%
G789164
8.1%
I588479
6.0%
M406063
 
4.2%
U368580
 
3.8%
Other values (13)1171536
12.0%
Common
ValueCountFrequency (%)
2796655
77.9%
-791240
 
22.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII13320108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2796655
21.0%
L1278291
9.6%
A1151829
8.6%
R1029204
 
7.7%
E1019529
 
7.7%
P970755
 
7.3%
O958783
 
7.2%
-791240
 
5.9%
G789164
 
5.9%
I588479
 
4.4%
Other values (15)1946179
14.6%

SEXO
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
M
413567 
F
396144 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters809711
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowF
4th rowM
5th rowF

Common Values

ValueCountFrequency (%)
M413567
51.1%
F396144
48.9%

Length

2022-08-07T13:14:38.007584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-07T13:14:38.100619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
m413567
51.1%
f396144
48.9%

Most occurring characters

ValueCountFrequency (%)
M413567
51.1%
F396144
48.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter809711
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M413567
51.1%
F396144
48.9%

Most occurring scripts

ValueCountFrequency (%)
Latin809711
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M413567
51.1%
F396144
48.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII809711
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M413567
51.1%
F396144
48.9%

SIT_PERSONA
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct41
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
DENUNCIADO
406431 
DENUNCIANTE
322532 
AGRAVIADO
44854 
DETENIDO
 
7051
INTERVENIDO
 
6987
Other values (36)
 
21856

Length

Max length24
Median length10
Mean length10.35570222
Min length4

Characters and Unicode

Total characters8385126
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowDENUNCIADO
2nd rowDENUNCIANTE
3rd rowDENUNCIANTE
4th rowDEPONENTE
5th rowDENUNCIANTE

Common Values

ValueCountFrequency (%)
DENUNCIADO406431
50.2%
DENUNCIANTE322532
39.8%
AGRAVIADO44854
 
5.5%
DETENIDO7051
 
0.9%
INTERVENIDO6987
 
0.9%
IMPLICADO4808
 
0.6%
RECURRENTE3790
 
0.5%
POR DETERMINAR2938
 
0.4%
SOLICITANTE1825
 
0.2%
PARTICIPANTES1655
 
0.2%
Other values (31)6840
 
0.8%

Length

2022-08-07T13:14:38.194614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
denunciado406431
49.9%
denunciante322532
39.6%
agraviado44854
 
5.5%
detenido7051
 
0.9%
intervenido6987
 
0.9%
implicado5046
 
0.6%
recurrente3790
 
0.5%
por2938
 
0.4%
determinar2938
 
0.4%
presunto2015
 
0.2%
Other values (31)10147
 
1.2%

Most occurring characters

ValueCountFrequency (%)
N1817020
21.7%
D1210651
14.4%
E1106723
13.2%
A883038
10.5%
I820677
9.8%
C744248
8.9%
U736477
8.8%
O481917
 
5.7%
T360017
 
4.3%
R80485
 
1.0%
Other values (13)143873
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter8380067
99.9%
Space Separator5018
 
0.1%
Other Punctuation41
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N1817020
21.7%
D1210651
14.4%
E1106723
13.2%
A883038
10.5%
I820677
9.8%
C744248
8.9%
U736477
8.8%
O481917
 
5.8%
T360017
 
4.3%
R80485
 
1.0%
Other values (11)138814
 
1.7%
Space Separator
ValueCountFrequency (%)
5018
100.0%
Other Punctuation
ValueCountFrequency (%)
.41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8380067
99.9%
Common5059
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
N1817020
21.7%
D1210651
14.4%
E1106723
13.2%
A883038
10.5%
I820677
9.8%
C744248
8.9%
U736477
8.8%
O481917
 
5.8%
T360017
 
4.3%
R80485
 
1.0%
Other values (11)138814
 
1.7%
Common
ValueCountFrequency (%)
5018
99.2%
.41
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII8385126
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N1817020
21.7%
D1210651
14.4%
E1106723
13.2%
A883038
10.5%
I820677
9.8%
C744248
8.9%
U736477
8.8%
O481917
 
5.7%
T360017
 
4.3%
R80485
 
1.0%
Other values (13)143873
 
1.7%

SUB_TIPO
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
LEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)
805878 
LEY DE PROTECCIÓN FRENTE A VIOLENCIA FAMILIAR (LEY 26260 25/06/97)
 
3833

Length

Max length126
Median length126
Mean length125.7159727
Min length66

Characters and Unicode

Total characters101793606
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLEY DE PROTECCIÓN FRENTE A VIOLENCIA FAMILIAR (LEY 26260 25/06/97)
2nd rowLEY DE PROTECCIÓN FRENTE A VIOLENCIA FAMILIAR (LEY 26260 25/06/97)
3rd rowLEY DE PROTECCIÓN FRENTE A VIOLENCIA FAMILIAR (LEY 26260 25/06/97)
4th rowLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)
5th rowLEY DE PROTECCIÓN FRENTE A VIOLENCIA FAMILIAR (LEY 26260 25/06/97)

Common Values

ValueCountFrequency (%)
LEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)805878
99.5%
LEY DE PROTECCIÓN FRENTE A VIOLENCIA FAMILIAR (LEY 26260 25/06/97)3833
 
0.5%

Length

2022-08-07T13:14:38.297584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-07T13:14:38.396614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
ley1619422
 
9.5%
y1611756
 
9.5%
familiar809711
 
4.8%
violencia809711
 
4.8%
mujeres805878
 
4.8%
30364805878
 
4.8%
nro805878
 
4.8%
grupo805878
 
4.8%
del805878
 
4.8%
integrantes805878
 
4.8%
Other values (15)7275900
42.9%

Most occurring characters

ValueCountFrequency (%)
16152057
15.9%
A10491746
10.3%
R9682035
 
9.5%
E8891489
 
8.7%
I6466189
 
6.4%
L6462356
 
6.3%
N6458523
 
6.3%
O4037056
 
4.0%
S4029390
 
4.0%
C3235011
 
3.2%
Other values (25)25887754
25.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter77525274
76.2%
Space Separator16152057
 
15.9%
Decimal Number4071553
 
4.0%
Lowercase Letter1611756
 
1.6%
Other Punctuation813544
 
0.8%
Open Punctuation809711
 
0.8%
Close Punctuation809711
 
0.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A10491746
13.5%
R9682035
12.5%
E8891489
11.5%
I6466189
8.3%
L6462356
8.3%
N6458523
8.3%
O4037056
 
5.2%
S4029390
 
5.2%
C3235011
 
4.2%
Y3231178
 
4.2%
Other values (10)14540301
18.8%
Decimal Number
ValueCountFrequency (%)
31611756
39.6%
6817377
20.1%
0813544
20.0%
4805878
19.8%
211499
 
0.3%
53833
 
0.1%
93833
 
0.1%
73833
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
r805878
50.0%
o805878
50.0%
Other Punctuation
ValueCountFrequency (%)
,805878
99.1%
/7666
 
0.9%
Space Separator
ValueCountFrequency (%)
16152057
100.0%
Open Punctuation
ValueCountFrequency (%)
(809711
100.0%
Close Punctuation
ValueCountFrequency (%)
)809711
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin79137030
77.7%
Common22656576
 
22.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A10491746
13.3%
R9682035
12.2%
E8891489
11.2%
I6466189
8.2%
L6462356
8.2%
N6458523
 
8.2%
O4037056
 
5.1%
S4029390
 
5.1%
C3235011
 
4.1%
Y3231178
 
4.1%
Other values (12)16152057
20.4%
Common
ValueCountFrequency (%)
16152057
71.3%
31611756
 
7.1%
6817377
 
3.6%
0813544
 
3.6%
(809711
 
3.6%
)809711
 
3.6%
,805878
 
3.6%
4805878
 
3.6%
211499
 
0.1%
/7666
 
< 0.1%
Other values (3)11499
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII101789773
> 99.9%
None3833
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16152057
15.9%
A10491746
10.3%
R9682035
 
9.5%
E8891489
 
8.7%
I6466189
 
6.4%
L6462356
 
6.3%
N6458523
 
6.3%
O4037056
 
4.0%
S4029390
 
4.0%
C3235011
 
3.2%
Other values (24)25883921
25.4%
None
ValueCountFrequency (%)
Ó3833
100.0%

TIPO
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
VIOLENCIA FAMILIAR
568362 
LEY DE VIOLENCIA CONTRA LA MUJER Y GRUPOS VULNERABLES
241349 

Length

Max length53
Median length18
Mean length28.43238266
Min length18

Characters and Unicode

Total characters23022013
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVIOLENCIA FAMILIAR
2nd rowVIOLENCIA FAMILIAR
3rd rowVIOLENCIA FAMILIAR
4th rowVIOLENCIA FAMILIAR
5th rowVIOLENCIA FAMILIAR

Common Values

ValueCountFrequency (%)
VIOLENCIA FAMILIAR568362
70.2%
LEY DE VIOLENCIA CONTRA LA MUJER Y GRUPOS VULNERABLES241349
29.8%

Length

2022-08-07T13:14:38.493584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-07T13:14:38.587614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
violencia809711
24.5%
familiar568362
17.2%
ley241349
 
7.3%
de241349
 
7.3%
contra241349
 
7.3%
la241349
 
7.3%
mujer241349
 
7.3%
y241349
 
7.3%
grupos241349
 
7.3%
vulnerables241349
 
7.3%

Most occurring characters

ValueCountFrequency (%)
I2756146
12.0%
A2670482
11.6%
2499154
10.9%
L2343469
10.2%
E2016456
8.8%
R1533758
 
6.7%
O1292409
 
5.6%
N1292409
 
5.6%
V1051060
 
4.6%
C1051060
 
4.6%
Other values (11)4515610
19.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter20522859
89.1%
Space Separator2499154
 
10.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I2756146
13.4%
A2670482
13.0%
L2343469
11.4%
E2016456
9.8%
R1533758
7.5%
O1292409
 
6.3%
N1292409
 
6.3%
V1051060
 
5.1%
C1051060
 
5.1%
M809711
 
3.9%
Other values (10)3705899
18.1%
Space Separator
ValueCountFrequency (%)
2499154
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin20522859
89.1%
Common2499154
 
10.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
I2756146
13.4%
A2670482
13.0%
L2343469
11.4%
E2016456
9.8%
R1533758
7.5%
O1292409
 
6.3%
N1292409
 
6.3%
V1051060
 
5.1%
C1051060
 
5.1%
M809711
 
3.9%
Other values (10)3705899
18.1%
Common
ValueCountFrequency (%)
2499154
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII23022013
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I2756146
12.0%
A2670482
11.6%
2499154
10.9%
L2343469
10.2%
E2016456
8.8%
R1533758
 
6.7%
O1292409
 
5.6%
N1292409
 
5.6%
V1051060
 
4.6%
C1051060
 
4.6%
Other values (11)4515610
19.6%

TIPO_DENUNCIA
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
DENUNCIA
505961 
ACTA DE DENUNCIA VERBAL
197304 
ACTA DE INTERVENCION
62576 
OCURRENCIA
 
43870

Length

Max length23
Median length8
Mean length12.69082426
Min length8

Characters and Unicode

Total characters10275900
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowACTA DE DENUNCIA VERBAL
2nd rowDENUNCIA
3rd rowDENUNCIA
4th rowACTA DE DENUNCIA VERBAL
5th rowDENUNCIA

Common Values

ValueCountFrequency (%)
DENUNCIA505961
62.5%
ACTA DE DENUNCIA VERBAL197304
 
24.4%
ACTA DE INTERVENCION62576
 
7.7%
OCURRENCIA43870
 
5.4%

Length

2022-08-07T13:14:38.678614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-07T13:14:38.780585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
denuncia703265
46.1%
acta259880
 
17.0%
de259880
 
17.0%
verbal197304
 
12.9%
intervencion62576
 
4.1%
ocurrencia43870
 
2.9%

Most occurring characters

ValueCountFrequency (%)
N1638128
15.9%
A1464199
14.2%
E1329471
12.9%
C1113461
10.8%
D963145
9.4%
I872287
8.5%
U747135
7.3%
717064
7.0%
R347620
 
3.4%
T322456
 
3.1%
Other values (4)760934
7.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter9558836
93.0%
Space Separator717064
 
7.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N1638128
17.1%
A1464199
15.3%
E1329471
13.9%
C1113461
11.6%
D963145
10.1%
I872287
9.1%
U747135
7.8%
R347620
 
3.6%
T322456
 
3.4%
V259880
 
2.7%
Other values (3)501054
 
5.2%
Space Separator
ValueCountFrequency (%)
717064
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9558836
93.0%
Common717064
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N1638128
17.1%
A1464199
15.3%
E1329471
13.9%
C1113461
11.6%
D963145
10.1%
I872287
9.1%
U747135
7.8%
R347620
 
3.6%
T322456
 
3.4%
V259880
 
2.7%
Other values (3)501054
 
5.2%
Common
ValueCountFrequency (%)
717064
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10275900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N1638128
15.9%
A1464199
14.2%
E1329471
12.9%
C1113461
10.8%
D963145
9.4%
I872287
8.5%
U747135
7.3%
717064
7.0%
R347620
 
3.4%
T322456
 
3.1%
Other values (4)760934
7.4%

UBICACION
Categorical

HIGH CARDINALITY

Distinct737837
Distinct (%)91.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
CALLE
 
331
SAN MARTIN
 
276
Unnamed Road, Perú
 
256
VIA PUBLICA
 
228
Cannot determine address at this location.
 
211
Other values (737832)
808409 

Length

Max length113
Median length79
Mean length36.44688043
Min length1

Characters and Unicode

Total characters29511440
Distinct characters152
Distinct categories17 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique710339 ?
Unique (%)87.7%

Sample

1st rowSECTOR SANTA ROSA MZ. 11 LT. 36 - HUANCHAQUITO ALTO
2nd rowMZ V LTE 17 SECTOR C AA HH LOMAS DE ZAPALLAL
3rd rowMZ A LTE 01 AA HH NUEVA ESPERANZA
4th rowaa.hh. palmeras del golf mz. b lTE. 15
5th rowPRONOI CARIÃ?â??OSITOS - ZAPALLAL PUENTE PIEDRA

Common Values

ValueCountFrequency (%)
CALLE331
 
< 0.1%
SAN MARTIN276
 
< 0.1%
Unnamed Road, Perú256
 
< 0.1%
VIA PUBLICA228
 
< 0.1%
Cannot determine address at this location.211
 
< 0.1%
VILLA MARIA DEL TRIUNFO196
 
< 0.1%
PACHACUTEC194
 
< 0.1%
SANTA ROSA179
 
< 0.1%
28 DE JULIO177
 
< 0.1%
TUPAC AMARU175
 
< 0.1%
Other values (737827)807488
99.7%

Length

2022-08-07T13:14:38.923585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mz208213
 
3.9%
de207135
 
3.8%
157429
 
2.9%
lote97797
 
1.8%
lt96162
 
1.8%
la86969
 
1.6%
av82987
 
1.5%
calle82981
 
1.5%
san75471
 
1.4%
jr67811
 
1.3%
Other values (159550)4244266
78.5%

Most occurring characters

ValueCountFrequency (%)
4672347
 
15.8%
A2744979
 
9.3%
E1538311
 
5.2%
O1428992
 
4.8%
L1407244
 
4.8%
R1290394
 
4.4%
N1087512
 
3.7%
I1041783
 
3.5%
S997672
 
3.4%
C985581
 
3.3%
Other values (142)12316625
41.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter17948286
60.8%
Space Separator4673145
 
15.8%
Lowercase Letter3557395
 
12.1%
Decimal Number1736005
 
5.9%
Other Punctuation1090063
 
3.7%
Dash Punctuation351670
 
1.2%
Other Symbol61061
 
0.2%
Open Punctuation32846
 
0.1%
Close Punctuation31113
 
0.1%
Other Letter17031
 
0.1%
Other values (7)12825
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A2744979
15.3%
E1538311
 
8.6%
O1428992
 
8.0%
L1407244
 
7.8%
R1290394
 
7.2%
N1087512
 
6.1%
I1041783
 
5.8%
S997672
 
5.6%
C985581
 
5.5%
T850368
 
4.7%
Other values (35)4575450
25.5%
Lowercase Letter
ValueCountFrequency (%)
a551363
15.5%
e345750
9.7%
o306273
 
8.6%
r296145
 
8.3%
l274026
 
7.7%
i214414
 
6.0%
n209828
 
5.9%
s181556
 
5.1%
c176662
 
5.0%
t168164
 
4.7%
Other values (32)833214
23.4%
Other Punctuation
ValueCountFrequency (%)
.800705
73.5%
,119519
 
11.0%
/69482
 
6.4%
?59059
 
5.4%
"25256
 
2.3%
:12669
 
1.2%
#1459
 
0.1%
;875
 
0.1%
¡520
 
< 0.1%
'374
 
< 0.1%
Other values (7)145
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1390009
22.5%
2256637
14.8%
0252088
14.5%
3175043
10.1%
4140170
 
8.1%
5133384
 
7.7%
6108338
 
6.2%
799227
 
5.7%
896706
 
5.6%
984403
 
4.9%
Math Symbol
ValueCountFrequency (%)
¬3632
81.4%
±513
 
11.5%
|239
 
5.4%
+49
 
1.1%
>10
 
0.2%
<9
 
0.2%
=5
 
0.1%
~3
 
0.1%
Other Number
ValueCountFrequency (%)
³522
80.6%
½97
 
15.0%
¹21
 
3.2%
¼4
 
0.6%
²4
 
0.6%
Modifier Symbol
ValueCountFrequency (%)
´443
73.5%
`100
 
16.6%
¨56
 
9.3%
^4
 
0.7%
Control
ValueCountFrequency (%)
264
42.7%
225
36.4%
123
19.9%
6
 
1.0%
Other Symbol
ValueCountFrequency (%)
°60822
99.6%
©220
 
0.4%
¦19
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
(32839
> 99.9%
{5
 
< 0.1%
[2
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
)31088
99.9%
}15
 
< 0.1%
]10
 
< 0.1%
Space Separator
ValueCountFrequency (%)
4672347
> 99.9%
 798
 
< 0.1%
Other Letter
ValueCountFrequency (%)
º15052
88.4%
ª1979
 
11.6%
Dash Punctuation
ValueCountFrequency (%)
-351670
100.0%
Currency Symbol
ValueCountFrequency (%)
¢5796
100.0%
Connector Punctuation
ValueCountFrequency (%)
_381
100.0%
Format
ValueCountFrequency (%)
­319
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin21522712
72.9%
Common7988728
 
27.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A2744979
 
12.8%
E1538311
 
7.1%
O1428992
 
6.6%
L1407244
 
6.5%
R1290394
 
6.0%
N1087512
 
5.1%
I1041783
 
4.8%
S997672
 
4.6%
C985581
 
4.6%
T850368
 
4.0%
Other values (79)8149876
37.9%
Common
ValueCountFrequency (%)
4672347
58.5%
.800705
 
10.0%
1390009
 
4.9%
-351670
 
4.4%
2256637
 
3.2%
0252088
 
3.2%
3175043
 
2.2%
4140170
 
1.8%
5133384
 
1.7%
,119519
 
1.5%
Other values (53)697156
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII29296612
99.3%
None214828
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4672347
 
15.9%
A2744979
 
9.4%
E1538311
 
5.3%
O1428992
 
4.9%
L1407244
 
4.8%
R1290394
 
4.4%
N1087512
 
3.7%
I1041783
 
3.6%
S997672
 
3.4%
C985581
 
3.4%
Other values (84)12101797
41.3%
None
ValueCountFrequency (%)
°60822
28.3%
Ã23602
 
11.0%
Ñ22377
 
10.4%
ú21176
 
9.9%
Â15169
 
7.1%
º15052
 
7.0%
â7649
 
3.6%
¢5796
 
2.7%
ó5386
 
2.5%
Ó4381
 
2.0%
Other values (48)33418
15.6%

VIA
Categorical

HIGH CORRELATION

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
Otros
467479 
Avenida
124553 
Calle
70912 
Jiron
60481 
AA.HH
 
20641
Other values (11)
65645 

Length

Max length14
Median length5
Mean length5.667985244
Min length4

Characters and Unicode

Total characters4589430
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOtros
2nd rowOtros
3rd rowOtros
4th rowOtros
5th rowOtros

Common Values

ValueCountFrequency (%)
Otros467479
57.7%
Avenida124553
 
15.4%
Calle70912
 
8.8%
Jiron60481
 
7.5%
AA.HH20641
 
2.5%
Pasaje16525
 
2.0%
Centro Poblado14783
 
1.8%
Urbanización12941
 
1.6%
Caserio7260
 
0.9%
Comunidad5097
 
0.6%
Other values (6)9039
 
1.1%

Length

2022-08-07T13:14:39.033584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
otros467479
56.7%
avenida124553
 
15.1%
calle70912
 
8.6%
jiron60481
 
7.3%
aa.hh20641
 
2.5%
pasaje16525
 
2.0%
centro14783
 
1.8%
poblado14783
 
1.8%
urbanización12941
 
1.6%
caserio7260
 
0.9%
Other values (7)14136
 
1.7%

Most occurring characters

ValueCountFrequency (%)
o587118
12.8%
r572413
12.5%
s492844
10.7%
t484667
10.6%
O467479
10.2%
a291805
 
6.4%
e243897
 
5.3%
n235500
 
5.1%
i225805
 
4.9%
A168287
 
3.7%
Other values (21)819615
17.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3667589
79.9%
Uppercase Letter886417
 
19.3%
Other Punctuation20641
 
0.4%
Space Separator14783
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o587118
16.0%
r572413
15.6%
s492844
13.4%
t484667
13.2%
a291805
8.0%
e243897
6.7%
n235500
6.4%
i225805
 
6.2%
l156955
 
4.3%
d149530
 
4.1%
Other values (11)227055
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
O467479
52.7%
A168287
 
19.0%
C100457
 
11.3%
J60481
 
6.8%
H41282
 
4.7%
P33562
 
3.8%
U14521
 
1.6%
M348
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.20641
100.0%
Space Separator
ValueCountFrequency (%)
14783
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4554006
99.2%
Common35424
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
o587118
12.9%
r572413
12.6%
s492844
10.8%
t484667
10.6%
O467479
10.3%
a291805
 
6.4%
e243897
 
5.4%
n235500
 
5.2%
i225805
 
5.0%
A168287
 
3.7%
Other values (19)784191
17.2%
Common
ValueCountFrequency (%)
.20641
58.3%
14783
41.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII4576141
99.7%
None13289
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o587118
12.8%
r572413
12.5%
s492844
10.8%
t484667
10.6%
O467479
10.2%
a291805
 
6.4%
e243897
 
5.3%
n235500
 
5.1%
i225805
 
4.9%
A168287
 
3.7%
Other values (20)806326
17.6%
None
ValueCountFrequency (%)
ó13289
100.0%

PAIS_NATAL
Categorical

HIGH CARDINALITY

Distinct211
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
PERU
804134 
VENEZUELA
 
2464
VENEZOLANO(A)
 
841
COLOMBIA
 
387
ARGENTINA
 
217
Other values (206)
 
1668

Length

Max length25
Median length4
Mean length4.03542869
Min length3

Characters and Unicode

Total characters3267531
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique107 ?
Unique (%)< 0.1%

Sample

1st rowPERU
2nd rowPERU
3rd rowPERU
4th rowPERU
5th rowPERU

Common Values

ValueCountFrequency (%)
PERU804134
99.3%
VENEZUELA2464
 
0.3%
VENEZOLANO(A)841
 
0.1%
COLOMBIA387
 
< 0.1%
ARGENTINA217
 
< 0.1%
ECUADOR190
 
< 0.1%
BOLIVIA158
 
< 0.1%
CHILE151
 
< 0.1%
ESPAÑA94
 
< 0.1%
BRASIL88
 
< 0.1%
Other values (201)987
 
0.1%

Length

2022-08-07T13:14:39.133584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
peru804135
99.3%
venezuela2465
 
0.3%
venezolano(a841
 
0.1%
colombia391
 
< 0.1%
argentina218
 
< 0.1%
ecuador191
 
< 0.1%
bolivia163
 
< 0.1%
chile154
 
< 0.1%
españa99
 
< 0.1%
brasil88
 
< 0.1%
Other values (209)1155
 
0.1%

Most occurring characters

ValueCountFrequency (%)
E814557
24.9%
U807253
24.7%
R805051
24.6%
P804373
24.6%
A7103
 
0.2%
N5343
 
0.2%
L4573
 
0.1%
V3619
 
0.1%
O3524
 
0.1%
Z3444
 
0.1%
Other values (20)8691
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter3265638
99.9%
Close Punctuation845
 
< 0.1%
Open Punctuation845
 
< 0.1%
Space Separator203
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E814557
24.9%
U807253
24.7%
R805051
24.7%
P804373
24.6%
A7103
 
0.2%
N5343
 
0.2%
L4573
 
0.1%
V3619
 
0.1%
O3524
 
0.1%
Z3444
 
0.1%
Other values (17)6798
 
0.2%
Close Punctuation
ValueCountFrequency (%)
)845
100.0%
Open Punctuation
ValueCountFrequency (%)
(845
100.0%
Space Separator
ValueCountFrequency (%)
203
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3265638
99.9%
Common1893
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E814557
24.9%
U807253
24.7%
R805051
24.7%
P804373
24.6%
A7103
 
0.2%
N5343
 
0.2%
L4573
 
0.1%
V3619
 
0.1%
O3524
 
0.1%
Z3444
 
0.1%
Other values (17)6798
 
0.2%
Common
ValueCountFrequency (%)
)845
44.6%
(845
44.6%
203
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII3267415
> 99.9%
None116
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E814557
24.9%
U807253
24.7%
R805051
24.6%
P804373
24.6%
A7103
 
0.2%
N5343
 
0.2%
L4573
 
0.1%
V3619
 
0.1%
O3524
 
0.1%
Z3444
 
0.1%
Other values (19)8575
 
0.3%
None
ValueCountFrequency (%)
Ñ116
100.0%

FEC_REGISTRO_ANIO
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
2019
289569 
2018
207252 
2017
179675 
2016
133213 
2014
 
2

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3238844
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2019289569
35.8%
2018207252
25.6%
2017179675
22.2%
2016133213
16.5%
20142
 
< 0.1%

Length

2022-08-07T13:14:39.229621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-07T13:14:39.330614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2019289569
35.8%
2018207252
25.6%
2017179675
22.2%
2016133213
16.5%
20142
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2809711
25.0%
0809711
25.0%
1809711
25.0%
9289569
 
8.9%
8207252
 
6.4%
7179675
 
5.5%
6133213
 
4.1%
42
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3238844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2809711
25.0%
0809711
25.0%
1809711
25.0%
9289569
 
8.9%
8207252
 
6.4%
7179675
 
5.5%
6133213
 
4.1%
42
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common3238844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2809711
25.0%
0809711
25.0%
1809711
25.0%
9289569
 
8.9%
8207252
 
6.4%
7179675
 
5.5%
6133213
 
4.1%
42
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3238844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2809711
25.0%
0809711
25.0%
1809711
25.0%
9289569
 
8.9%
8207252
 
6.4%
7179675
 
5.5%
6133213
 
4.1%
42
 
< 0.1%

FEC_REGISTRO_MES
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.570191093
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2022-08-07T13:14:39.420614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.556472892
Coefficient of variation (CV)0.5413043307
Kurtosis-1.30146072
Mean6.570191093
Median Absolute Deviation (MAD)3
Skewness0.01865577497
Sum5319956
Variance12.64849943
MonotonicityNot monotonic
2022-08-07T13:14:39.499584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1280066
9.9%
1176406
9.4%
1074636
9.2%
373871
9.1%
470909
8.8%
569278
8.6%
868356
8.4%
266856
8.3%
666456
8.2%
166445
8.2%
Other values (2)96432
11.9%
ValueCountFrequency (%)
166445
8.2%
266856
8.3%
373871
9.1%
470909
8.8%
569278
8.6%
666456
8.2%
746465
5.7%
868356
8.4%
949967
6.2%
1074636
9.2%
ValueCountFrequency (%)
1280066
9.9%
1176406
9.4%
1074636
9.2%
949967
6.2%
868356
8.4%
746465
5.7%
666456
8.2%
569278
8.6%
470909
8.8%
373871
9.1%

FEC_REGISTRO_DIA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.76319205
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2022-08-07T13:14:39.592617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.78737789
Coefficient of variation (CV)0.5574618301
Kurtosis-1.191214714
Mean15.76319205
Median Absolute Deviation (MAD)8
Skewness0.002019229053
Sum12763630
Variance77.21801018
MonotonicityNot monotonic
2022-08-07T13:14:39.684613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1827588
 
3.4%
427338
 
3.4%
1927238
 
3.4%
2227226
 
3.4%
2627043
 
3.3%
1127001
 
3.3%
2826994
 
3.3%
2026906
 
3.3%
526839
 
3.3%
1726780
 
3.3%
Other values (21)538758
66.5%
ValueCountFrequency (%)
125421
3.1%
226617
3.3%
326570
3.3%
427338
3.4%
526839
3.3%
626451
3.3%
726245
3.2%
825855
3.2%
926606
3.3%
1026099
3.2%
ValueCountFrequency (%)
3115991
2.0%
3023951
3.0%
2924705
3.1%
2826994
3.3%
2726550
3.3%
2627043
3.3%
2526303
3.2%
2425920
3.2%
2326493
3.3%
2227226
3.4%

FEC_REGISTRO_DIA_SEM
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.793853609
Minimum0
Maximum6
Zeros139264
Zeros (%)17.2%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2022-08-07T13:14:39.771585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.011049755
Coefficient of variation (CV)0.7198121433
Kurtosis-1.231037702
Mean2.793853609
Median Absolute Deviation (MAD)2
Skewness0.1337823363
Sum2262214
Variance4.044321116
MonotonicityNot monotonic
2022-08-07T13:14:39.843587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0139264
17.2%
1123634
15.3%
2119862
14.8%
3115677
14.3%
4109496
13.5%
6104951
13.0%
596827
12.0%
ValueCountFrequency (%)
0139264
17.2%
1123634
15.3%
2119862
14.8%
3115677
14.3%
4109496
13.5%
596827
12.0%
6104951
13.0%
ValueCountFrequency (%)
6104951
13.0%
596827
12.0%
4109496
13.5%
3115677
14.3%
2119862
14.8%
1123634
15.3%
0139264
17.2%

FECHA_HORA_HECHO_ANIO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.795316
Minimum1990
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2022-08-07T13:14:39.934586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1990
5-th percentile2016
Q12017
median2018
Q32019
95-th percentile2019
Maximum2019
Range29
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.114634399
Coefficient of variation (CV)0.0005524021144
Kurtosis3.45860723
Mean2017.795316
Median Absolute Deviation (MAD)1
Skewness-0.6262832874
Sum1633831063
Variance1.242409843
MonotonicityNot monotonic
2022-08-07T13:14:40.022587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2019288899
35.7%
2018204968
25.3%
2017179819
22.2%
2016134482
16.6%
20151292
 
0.2%
201476
 
< 0.1%
201333
 
< 0.1%
201228
 
< 0.1%
201122
 
< 0.1%
200718
 
< 0.1%
Other values (15)74
 
< 0.1%
ValueCountFrequency (%)
19905
< 0.1%
19961
 
< 0.1%
19973
< 0.1%
19981
 
< 0.1%
19991
 
< 0.1%
20003
< 0.1%
20012
 
< 0.1%
20024
< 0.1%
20033
< 0.1%
20044
< 0.1%
ValueCountFrequency (%)
2019288899
35.7%
2018204968
25.3%
2017179819
22.2%
2016134482
16.6%
20151292
 
0.2%
201476
 
< 0.1%
201333
 
< 0.1%
201228
 
< 0.1%
201122
 
< 0.1%
201010
 
< 0.1%

FECHA_HORA_HECHO_MES
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.520591915
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2022-08-07T13:14:40.113620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.558400676
Coefficient of variation (CV)0.5457174322
Kurtosis-1.301972543
Mean6.520591915
Median Absolute Deviation (MAD)3
Skewness0.02634332052
Sum5279795
Variance12.66221537
MonotonicityNot monotonic
2022-08-07T13:14:40.386585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1176507
9.4%
1276396
9.4%
374629
9.2%
1073872
9.1%
470337
8.7%
169762
8.6%
568663
8.5%
267501
8.3%
866480
8.2%
665162
8.0%
Other values (2)100402
12.4%
ValueCountFrequency (%)
169762
8.6%
267501
8.3%
374629
9.2%
470337
8.7%
568663
8.5%
665162
8.0%
748499
6.0%
866480
8.2%
951903
6.4%
1073872
9.1%
ValueCountFrequency (%)
1276396
9.4%
1176507
9.4%
1073872
9.1%
951903
6.4%
866480
8.2%
748499
6.0%
665162
8.0%
568663
8.5%
470337
8.7%
374629
9.2%

FECHA_HORA_HECHO_DIA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.6089506
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2022-08-07T13:14:40.481585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.812234928
Coefficient of variation (CV)0.5645629327
Kurtosis-1.192272551
Mean15.6089506
Median Absolute Deviation (MAD)8
Skewness0.009148324343
Sum12638739
Variance77.65548442
MonotonicityNot monotonic
2022-08-07T13:14:40.575585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
129807
 
3.7%
2527994
 
3.5%
1727556
 
3.4%
327424
 
3.4%
1527382
 
3.4%
1827250
 
3.4%
227145
 
3.4%
427134
 
3.4%
1126789
 
3.3%
1026725
 
3.3%
Other values (21)534505
66.0%
ValueCountFrequency (%)
129807
3.7%
227145
3.4%
327424
3.4%
427134
3.4%
526256
3.2%
625957
3.2%
726000
3.2%
826366
3.3%
926200
3.2%
1026725
3.3%
ValueCountFrequency (%)
3114322
1.8%
3023617
2.9%
2924371
3.0%
2826350
3.3%
2726113
3.2%
2626227
3.2%
2527994
3.5%
2426531
3.3%
2325652
3.2%
2225885
3.2%

FECHA_HORA_HECHO_DIA_SEM
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.129520286
Minimum0
Maximum6
Zeros123397
Zeros (%)15.2%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2022-08-07T13:14:40.662615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.108682869
Coefficient of variation (CV)0.6738038665
Kurtosis-1.352700014
Mean3.129520286
Median Absolute Deviation (MAD)2
Skewness-0.05818490955
Sum2534007
Variance4.446543441
MonotonicityNot monotonic
2022-08-07T13:14:40.734614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6158198
19.5%
0123397
15.2%
5110623
13.7%
1107474
13.3%
2106539
13.2%
3102768
12.7%
4100712
12.4%
ValueCountFrequency (%)
0123397
15.2%
1107474
13.3%
2106539
13.2%
3102768
12.7%
4100712
12.4%
5110623
13.7%
6158198
19.5%
ValueCountFrequency (%)
6158198
19.5%
5110623
13.7%
4100712
12.4%
3102768
12.7%
2106539
13.2%
1107474
13.3%
0123397
15.2%

Interactions

2022-08-07T13:14:24.478568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:06.926591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:10.006241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:12.518382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:14.912384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:17.214382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:19.641383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:22.004568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:24.781569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:07.476054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:10.297240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:12.808382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:15.198383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:17.611383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:19.924420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:22.311568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:25.084566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:07.805114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:10.629242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:13.096383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:15.484383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:17.895383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:20.212448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:22.617566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:25.386567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:08.145120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:10.960243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:13.395382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:15.767383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:18.183388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:20.508565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:22.924565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:25.695565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:08.516121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:11.293317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:13.696444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:16.053384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:18.465382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:20.795566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:23.231570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:25.998566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:08.897120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:11.623383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:13.994382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:16.344383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:18.755382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:21.082567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:23.540566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:26.305567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:09.288119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:11.930382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:14.293385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:16.632384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:19.046383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:21.383568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:23.852567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:26.607566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:09.654121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:12.219382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:14.610382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:16.923381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:19.343385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:21.686566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T13:14:24.165566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-08-07T13:14:40.820613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-07T13:14:40.963585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-07T13:14:41.104584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-07T13:14:41.252616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-08-07T13:14:41.409585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-07T13:14:28.483596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-07T13:14:31.917585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

COMISARIADERIVADA_FISCALIADIRECCIONDIST_CIADIST_HECHODPTO_CIADPTO_HECHOEDADEST_CIVILLIBROMODALIDADPROV_CIAPROV_HECHOREGIONSEXOSIT_PERSONASUB_TIPOTIPOTIPO_DENUNCIAUBICACIONVIAPAIS_NATALFEC_REGISTRO_ANIOFEC_REGISTRO_MESFEC_REGISTRO_DIAFEC_REGISTRO_DIA_SEMFECHA_HORA_HECHO_ANIOFECHA_HORA_HECHO_MESFECHA_HORA_HECHO_DIAFECHA_HORA_HECHO_DIA_SEM
0HUANCHACOJUZGADO DE FAMILIAAV. LA RIVERA PLAZA SAN MARTINHUANCHACOHUANCHACOLA LIBERTADLA LIBERTAD32CONVIVIENTE[DEINPOL] ACTA DE DENUNCIA VERBALMALTRATO SIN LESIONTRUJILLOTRUJILLOREGPOL - LA LIBERTADMDENUNCIADOLEY DE PROTECCIÓN FRENTE A VIOLENCIA FAMILIAR (LEY 26260 25/06/97)VIOLENCIA FAMILIARACTA DE DENUNCIA VERBALSECTOR SANTA ROSA MZ. 11 LT. 36 - HUANCHAQUITO ALTOOtrosPERU20161142016114
1ZAPALLALUNIDAD PNPJR GALILEA S/NPUENTE PIEDRAPUENTE PIEDRALIMALIMA25SOLTERO(A)[FAM] DENUNCIA VIOLENCIA FAMILIARMALTRATO SIN LESIONLIMALIMAREGPOL - LIMAFDENUNCIANTELEY DE PROTECCIÓN FRENTE A VIOLENCIA FAMILIAR (LEY 26260 25/06/97)VIOLENCIA FAMILIARDENUNCIAMZ V LTE 17 SECTOR C AA HH LOMAS DE ZAPALLALOtrosPERU20161142016114
2ZAPALLALUNIDAD PNPJR GALILEA S/NPUENTE PIEDRAPUENTE PIEDRALIMALIMA20SOLTERO(A)[FAM] DENUNCIA VIOLENCIA FAMILIARMALTRATO SIN LESIONLIMALIMAREGPOL - LIMAFDENUNCIANTELEY DE PROTECCIÓN FRENTE A VIOLENCIA FAMILIAR (LEY 26260 25/06/97)VIOLENCIA FAMILIARDENUNCIAMZ A LTE 01 AA HH NUEVA ESPERANZAOtrosPERU20161142016114
3COMISARIA DE LA FAMILIAJUZGADO DE FAMILIAURB. CIUDAD DE DIOS MZA. Q LTE 1NUEVO CHIMBOTENUEVO CHIMBOTEANCASHANCASH25SOLTERO(A)[FAM] ACTA DE DENUNCIA VERBALVIOLENCIA FISICASANTASANTAREGPOL - HUARAZMDEPONENTELEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)VIOLENCIA FAMILIARACTA DE DENUNCIA VERBALaa.hh. palmeras del golf mz. b lTE. 15OtrosPERU20161142016114
4ZAPALLALUNIDAD PNPJR GALILEA S/NPUENTE PIEDRAPUENTE PIEDRALIMALIMA51SOLTERO(A)[FAM] DENUNCIA VIOLENCIA FAMILIARMALTRATO SIN LESIONLIMALIMAREGPOL - LIMAFDENUNCIANTELEY DE PROTECCIÓN FRENTE A VIOLENCIA FAMILIAR (LEY 26260 25/06/97)VIOLENCIA FAMILIARDENUNCIAPRONOI CARIÃ?â??OSITOS - ZAPALLAL PUENTE PIEDRAOtrosPERU20161142016114
5AEROPUERTO VELASCO ASTETEJUZGADO DE FAMILIAAV. ALEJANDRO VELASCO ASTETEWANCHAQSAN SEBASTIANCUSCOCUSCO39CASADO(A)[FAM] ACTA DE DENUNCIA VERBALMALTRATO SIN LESIONCUSCOCUSCOREGPOL - CUSCOMDENUNCIANTELEY DE PROTECCIÓN FRENTE A VIOLENCIA FAMILIAR (LEY 26260 25/06/97)VIOLENCIA FAMILIARACTA DE DENUNCIA VERBALAPV Patron San Sebastian E-12OtrosPERU20161142016114
6MONTERRICOOTROSAV. MANUEL OLGUIN CUADRA 6 URB. LOS GRANADOSSANTIAGO DE SURCOLINCELIMALIMA31SOLTERO(A)[FAM] DENUNCIA VIOLENCIA FAMILIARVIOLENCIA PSICOLOGICALIMALIMAREGPOL - LIMAFDENUNCIANTELEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)VIOLENCIA FAMILIARDENUNCIACALLE ALBERTO ALEXANDER NRO. 2229 DPTO.302OtrosPERU2016125201512254
7DULANTOJUZGADO DE FAMILIACALLAOCALLAOCALLAOCALLAOCALLAO40SOLTERO(A)[FAM] DENUNCIA VIOLENCIA FAMILIARMALTRATO SIN LESIONCALLAOCALLAOREGPOL - CALLAOMDENUNCIADOLEY DE PROTECCIÓN FRENTE A VIOLENCIA FAMILIAR (LEY 26260 25/06/97)VIOLENCIA FAMILIARDENUNCIAMZ. 2 LOTE 31OtrosPERU20161252016114
8CHACARILLAJUZGADO DE FAMILIAJR. ALFREDO GALEON CDRA 3 CHACARRILLASAN BORJASAN BORJALIMALIMA24SOLTERO(A)[FAM] DENUNCIA VIOLENCIA FAMILIARVIOLENCIA FISICA Y PSICOLOGICALIMALIMAREGPOL - LIMAFDENUNCIANTELEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)VIOLENCIA FAMILIARDENUNCIAPASEO DEL BOSQUEOtrosPERU20161362016151
9JICAMARCAOTROSMz. Q S/N Ovalo Principal de JicamarcaLURIGANCHO - CHOSICALURIGANCHO - CHOSICALIMALIMA28CONVIVIENTE[FAM] DENUNCIA VIOLENCIA FAMILIARMALTRATO SIN LESIONLIMALIMAREGPOL - LIMAFDENUNCIADOLEY DE PROTECCIÓN FRENTE A VIOLENCIA FAMILIAR (LEY 26260 25/06/97)VIOLENCIA FAMILIARDENUNCIASANTA CRUZOtrosPERU20161362016125

Last rows

COMISARIADERIVADA_FISCALIADIRECCIONDIST_CIADIST_HECHODPTO_CIADPTO_HECHOEDADEST_CIVILLIBROMODALIDADPROV_CIAPROV_HECHOREGIONSEXOSIT_PERSONASUB_TIPOTIPOTIPO_DENUNCIAUBICACIONVIAPAIS_NATALFEC_REGISTRO_ANIOFEC_REGISTRO_MESFEC_REGISTRO_DIAFEC_REGISTRO_DIA_SEMFECHA_HORA_HECHO_ANIOFECHA_HORA_HECHO_MESFECHA_HORA_HECHO_DIAFECHA_HORA_HECHO_DIA_SEM
809701TACNA - NATIVIDADJUZGADO DE FAMILIAcalle nuestra señora de natividad nro 1937 cpm la natividadTACNATACNATACNATACNA50SOLTERO(A)[FAM] ACTA DE INTERVENCIONVIOLENCIA PSICOLOGICATACNATACNAREGPOL - TACNAMDENUNCIADOLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)LEY DE VIOLENCIA CONTRA LA MUJER Y GRUPOS VULNERABLESACTA DE INTERVENCIONcalle carolina freyre 2273-A NATIVIDADOtrosPERU201912160201912156
809702TACNA - NATIVIDADJUZGADO DE FAMILIAcalle nuestra señora de natividad nro 1937 cpm la natividadTACNATACNATACNATACNA33CASADO(A)[FAM] ACTA DE DENUNCIA VERBALVIOLENCIA PSICOLOGICATACNATACNAREGPOL - TACNAFDENUNCIANTELEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)LEY DE VIOLENCIA CONTRA LA MUJER Y GRUPOS VULNERABLESACTA DE DENUNCIA VERBALEN EL INTERIOR DE LA CANCHA DE FÚTBOL VIDENITA CP LA NATIVIDADOtrosPERU201912182201912145
809703TACNA - NATIVIDADJUZGADO DE FAMILIAcalle nuestra señora de natividad nro 1937 cpm la natividadTACNATACNATACNATACNA36SOLTERO(A)[FAM] ACTA DE DENUNCIA VERBALVIOLENCIA PSICOLOGICATACNATACNAREGPOL - TACNAMDENUNCIADOLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)LEY DE VIOLENCIA CONTRA LA MUJER Y GRUPOS VULNERABLESACTA DE DENUNCIA VERBALCALLE NATIVIDAD NRO.2127 CP LA NATIVIDAD - TACNAOtrosPERU201912182201912171
809704TACNA - NATIVIDADJUZGADO DE FAMILIAcalle nuestra señora de natividad nro 1937 cpm la natividadTACNATACNATACNATACNA56SOLTERO(A)[FAM] ACTA DE DENUNCIA VERBALVIOLENCIA PSICOLOGICATACNATACNAREGPOL - TACNAMDENUNCIADOLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)LEY DE VIOLENCIA CONTRA LA MUJER Y GRUPOS VULNERABLESACTA DE DENUNCIA VERBALCALLE CRISTINA VILDOSO NRO, 1799 - CP. NATIVIDADOtrosPERU20191222620197312
809705TACNA - NATIVIDADOTROScalle nuestra señora de natividad nro 1937 cpm la natividadTACNATACNATACNATACNA59SOLTERO(A)[FAM] ACTA DE DENUNCIA VERBALVIOLENCIA PSICOLOGICATACNATACNAREGPOL - TACNAMDENUNCIADOLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)LEY DE VIOLENCIA CONTRA LA MUJER Y GRUPOS VULNERABLESACTA DE DENUNCIA VERBALAlfonso Ugarte II Etapa Mz. C ? 01 Lote 03 ? Gregorio Albarracín TacnaOtrosPERU201912263201912252
809706TACNA - NATIVIDADJUZGADO DE FAMILIAcalle nuestra señora de natividad nro 1937 cpm la natividadTACNATACNATACNATACNA30SOLTERO(A)[FAM] ACTA DE INTERVENCIONVIOLENCIA PSICOLOGICATACNATACNAREGPOL - TACNAMDENUNCIADOLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)LEY DE VIOLENCIA CONTRA LA MUJER Y GRUPOS VULNERABLESACTA DE INTERVENCIONCALLE JUAN PABLO VIZCARDO Y GUZMAN NRO.1673 CP LA NATIVIDADOtrosPERU201912300201912263
809707TACNA - NATIVIDADOTROScalle nuestra señora de natividad nro 1937 cpm la natividadTACNATACNATACNATACNA22SOLTERO(A)[FAM] ACTA DE DENUNCIA VERBALVIOLENCIA PSICOLOGICATACNATACNAREGPOL - TACNAMDENUNCIADOLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)LEY DE VIOLENCIA CONTRA LA MUJER Y GRUPOS VULNERABLESACTA DE DENUNCIA VERBALCalle Moquegua Nro. 242OtrosPERU201912311201912311
809708TARATAJUZGADO DE FAMILIACALLE PRIMERO DE SETIEMBRE S/N TARATATARATATARATATACNATACNA38SOLTERO(A)[FAM] ACTA DE DENUNCIA VERBALVIOLENCIA FISICATARATATARATAREGPOL - TACNAMDENUNCIADOLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)LEY DE VIOLENCIA CONTRA LA MUJER Y GRUPOS VULNERABLESACTA DE DENUNCIA VERBALCalle Bolognesi S/N - TarataOtrosPERU20191290201911250
809709TARATAJUZGADO DE FAMILIACALLE PRIMERO DE SETIEMBRE S/N TARATATARATATARATATACNATACNA36SOLTERO(A)[DEINPOL] DENUNCIA DIRECTA DELITOVIOLENCIA FISICATARATATARATAREGPOL - TACNAMAGRESORLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)LEY DE VIOLENCIA CONTRA LA MUJER Y GRUPOS VULNERABLESDENUNCIACalle San Martin S/N TarataOtrosPERU201912230201912226
809710TARATAJUZGADO DE FAMILIACALLE PRIMERO DE SETIEMBRE S/N TARATATARATATARATATACNATACNA37CONVIVIENTE[DEINPOL] DENUNCIA DIRECTA DELITOVIOLENCIA FISICATARATATARATAREGPOL - TACNAMDETENIDOLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)LEY DE VIOLENCIA CONTRA LA MUJER Y GRUPOS VULNERABLESDENUNCIAcalle San Martin S/N TarataOtrosPERU201912285201912274

Duplicate rows

Most frequently occurring

COMISARIADERIVADA_FISCALIADIRECCIONDIST_CIADIST_HECHODPTO_CIADPTO_HECHOEDADEST_CIVILLIBROMODALIDADPROV_CIAPROV_HECHOREGIONSEXOSIT_PERSONASUB_TIPOTIPOTIPO_DENUNCIAUBICACIONVIAPAIS_NATALFEC_REGISTRO_ANIOFEC_REGISTRO_MESFEC_REGISTRO_DIAFEC_REGISTRO_DIA_SEMFECHA_HORA_HECHO_ANIOFECHA_HORA_HECHO_MESFECHA_HORA_HECHO_DIAFECHA_HORA_HECHO_DIA_SEM# duplicates
221 DE ABRILOTROSAv. 21 DE ABRIL ZONA B MZ.V LT.2CHIMBOTECHIMBOTEANCASHANCASH31NO INDICA[FAM] ACTA DE DENUNCIA VERBALVIOLENCIA FISICA Y PSICOLOGICASANTASANTAREGPOL - HUARAZMDENUNCIANTELEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)VIOLENCIA FAMILIARACTA DE DENUNCIA VERBALAV. AVIACION PSJ. DOS DE MAYOAvenidaPERU2016811320168807
209COMISARIA DE LA FAMILIAJUZGADO DE FAMILIAAV. BALTA 080CHICLAYOCHICLAYOLAMBAYEQUELAMBAYEQUE51SOLTERO(A)[FAM] DENUNCIA VIOLENCIA FAMILIARVIOLENCIA PSICOLOGICACHICLAYOCHICLAYOREGPOL - LAMBAYEQUEFDENUNCIANTELEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)VIOLENCIA FAMILIARDENUNCIAMZ E LT. JOVEN LA MOLINA ALTACallePERU20168172201681617
520MARIANO MELGAROTROSplaza umachiriMARIANO MELGARMARIANO MELGARAREQUIPAAREQUIPA32CONVIVIENTE[FAM] ACTA DE DENUNCIA VERBALVIOLENCIA PSICOLOGICAAREQUIPAAREQUIPAREGPOL - AREQUIPAFDENUNCIANTELEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)VIOLENCIA FAMILIARACTA DE DENUNCIA VERBALSEPULVEDA Nº 811AvenidaPERU20168216201682167
861VICTOR RAUL VIRUOTROSAV. VIRU N° 125 2DO PISOVIRUVIRULA LIBERTADLA LIBERTAD18SOLTERO(A)[FAM] DENUNCIA VIOLENCIA FAMILIARVIOLENCIA FISICA Y PSICOLOGICAVIRUVIRUREGPOL - LA LIBERTADFDENUNCIANTELEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)VIOLENCIA FAMILIARDENUNCIAVICTOR RAULCentro PobladoPERU2018111422018111426
521MARIANO MELGAROTROSplaza umachiriMARIANO MELGARMARIANO MELGARAREQUIPAAREQUIPA57SOLTERO(A)[FAM] ACTA DE DENUNCIA VERBALVIOLENCIA PSICOLOGICAAREQUIPAAREQUIPAREGPOL - AREQUIPAMDENUNCIANTELEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)VIOLENCIA FAMILIARACTA DE DENUNCIA VERBALen la puerta de su domicilio sito en la calle berna nº 229CallePERU20167202201672025
751SECHURAOTROSCALLE SUCRE 217-SECHURASECHURASECHURAPIURAPIURA35NO INDICA[FAM] DENUNCIA VIOLENCIA FAMILIARVIOLENCIA PSICOLOGICASECHURASECHURAREGPOL - PIURAMRECURRENTELEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)VIOLENCIA FAMILIARDENUNCIABOLIVAR Nº501CallePERU20171252201712525
807TAHUANTINSUYOJUZGADO DE FAMILIAPLAZA LAS AMERICAS S/NCUSCOCUSCOCUSCOCUSCO41SOLTERO(A)[FAM] ACTA DE INTERVENCIONVIOLENCIA FISICACUSCOCUSCOREGPOL - CUSCOFSOLICITANTELEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)VIOLENCIA FAMILIARACTA DE INTERVENCIONAV. ARGENTINA B-2 CUSCO.AvenidaPERU20172104201721045
726 DE OCTUBREOTROSCCJ HABITACIONAL TERCERA ETAPA MICALEA BASTIDAS ENACE26 DE OCTUBRE26 DE OCTUBREPIURAPIURA33SOLTERO(A)[FAM] ACTA DE DENUNCIA VERBALVIOLENCIA PSICOLOGICAPIURAPIURAREGPOL - PIURAFDENUNCIANTELEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)VIOLENCIA FAMILIARACTA DE DENUNCIA VERBALAMPLIACION ALEDAÑOS KURT BEER MZ C1´ LTE 41OtrosPERU20183190201831864
837UCHUMAYO – CONGATAOTROSURB. EL CARMEN B-2Y3UCHUMAYOUCHUMAYOAREQUIPAAREQUIPA40SOLTERO(A)[FAM] DENUNCIA VIOLENCIA FAMILIARVIOLENCIA PSICOLOGICAAREQUIPAAREQUIPAREGPOL - AREQUIPAMDENUNCIADOLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)VIOLENCIA FAMILIARDENUNCIAJUAN EL BUENO II MZ.B LT17 CONGATAOtrosPERU20183190201831754
106CASIMIRO CUADROJUZGADO DE FAMILIACASIMIRO CUADROSCAYMACAYMAAREQUIPAAREQUIPA28SOLTERO(A)[FAM] DENUNCIA VIOLENCIA FAMILIAR - RESERVADAVIOLENCIA FISICA Y PSICOLOGICAAREQUIPAAREQUIPAREGPOL - AREQUIPAMDENUNCIADOLEY PARA PREVENIR , SANCIONAR Y ERRADICAR LA VIOLENCIA CONTRA LAS MUJERES Y LOS INTEGRANTES DEL GRUPO FAMILIAR (LEY Nro 30364)VIOLENCIA FAMILIARDENUNCIAVIRGEN DE LA CANDELARIA MZ. A LOTE. 2 BUENOS AIRES CAYMAOtrosPERU20167235201672243